In this study, we introduce an innovative Robot State Estimation (RSE) methodology incorporating a learning-based contact estimation framework for legged robots, which obviates the need for external physical contact sensors. This approach integrates multimodal proprioceptive sensory data, employing a Physics-Informed Neural Network (PINN) in conjunction with an Unscented Kalman Filter (UKF) to enhance the state estimation process. The primary objective of this RSE technique is to calibrate the Inertial Measurement Unit (IMU) effectively and furnish a detailed representation of the robot’s dynamic state. Our methodology exploits the PINN to mitigate IMU drift issues by imposing constraints on the loss function via Ordinary Differential Equations (ODEs). The advantages of utilizing a contact estimator based on proprioceptive sensory data are multifold. Unlike vision-based state estimators, our proprioceptive approach is immune to visual impairments such as obscured or ambiguous environments. Moreover, it circumvents the necessity for dedicated contact sensors—components not universally present on robotic platforms and challenging to integrate without substantial hardware modifications. The contact estimator within our network is trained to discern contact events across various terrains, thereby facilitating resilient proprioceptive odometry. This enables the contact-aided invariant Kalman Filter to produce precise odometric trajectories. Subsequently, the UKF algorithm estimates the robot’s three-dimensional attitude, velocity, and position. Experimental validation of our proposed PINN-based method illustrates its capacity to assimilate data from multiple sensors, effectively reducing the influence of sensor biases by enforcing ODE constraints, all while preserving intrinsic sensor characteristics. When juxtaposed with the employment of the UKF algorithm in isolation, our integrated RSE model demonstrates superior performance in state estimation. This enhanced capability automatically reduces sensor drift impacts during operational deployment, rendering our proposed solution applicable to real-world scenarios.
The mobility and versatility of Unmanned Aerial Systems (UASs) make them valuable platforms in Distributed Cooperative Beamforming (DCB) applications, where high-precision time synchronization and Positioning, Navigation, and Timing (PNT) are required. UAS with PNT can quickly respond to changing situations and provide temporary coverage in remote or disaster-affected areas. While the onboard PNT equipment allows UASs to obtain reliable PNT solutions, human presence with supervisory roles (aka human-on-the-loop (HotL)) is almost inevitable in such equipment with automation and multi-level resilience of prevention, response, and recovery functions. This paper employs a meta-model to describe interactions among the human operators and multiple UAS platforms for resilience aware HotL PNT in the DCB scenario. The roles of UASs and humans in the decision-making process of resilient PNT are clarified. Interaction points where humans should collaborate with UASs are identified to augment the autonomy of the UASs. Moreover, requirements are specified for the interaction points. Simulations of a HotL multi-UAS positioning system demonstrate that the requirements modeling facilitates the design of human-machine teaming, and the human presence enhances the resilience of the positioning system.
Interference of satellite communications is a frequent and ongoing concern for both DoD and civilian enterprises. Geolocation of the interfering source is an essential step in mitigating or eliminating the interference and restoring the operation of the communications service. Doppler information is useful for the passive geolocation of ground-based EMI sources. This paper proposes a cross-correlation-based method to blindly estimate the EMI Doppler information from a single satellite. In the proposed blind estimation method, the carrier and waveforms of the EMI sources are relaxed. Simulated numerical results demonstrate the effectiveness and accuracy of cross-correlation-based blind Doppler estimation
KEYWORDS: Signal to noise ratio, Data modeling, Telecommunications, Signal processing, Education and training, Denoising, Deep learning, Tunable filters, Feature extraction, Interference (communication)
The scarcity and finite nature of the wireless spectrum drives technology development for spectrum utilization. With the increased complexity of the radio-access environment and susceptibility to interference disruption, challenges exist which demand advanced interference suppression techniques. Recently, the advance of artificial intelligence (AI) promotes technology for data-driven modeling of complicated relationships, which provides numerous tools and techniques for signal processing and analysis. This paper develops a deep learning-based radio signal interference suppression method by leveraging the adaptive features and Convolutional Neural Network (CNN) based Denoising autoencoder (DAE). By simulating the communication system with stochastic channel effects (AWGN channel), the proposed Suppression of Interference DEA (S-IDEA) method is validated using the original signals and the corrupted signals through channel effects. The results show that S-IDEA can effectively perform interference suppression from AWGN channel at different SNR levels and achieve excellent SNR improvement.
This paper outlines our approach to solving the amplitude modulation-to-amplitude modulation (AM-AM), and amplitude modulation-to-phase modulation (AM-PM) distortions caused by the onboard high-power amplifier (HPA) operating at the saturation point. The approach employs machine learning and artificial intelligence (ML-AI) to predistort the input signal such that the output of the post-HPA pre-distorted signal is identical to the original. The proposed ML-AI approach utilizes an existing MATLAB reinforcement learning technique using Deep Deterministic Policy Gradient (DDPG). The bulk of the research was to incorporate the proposed DDPG pre-distorter into the newly developed GNSS Single Side Band-Multi-Carrier Broadband Waveforms (SSB-MCBBW) and tune the pre-distorter’s hyper-parameters. The fine-tuning process was achieved efficiently by utilizing the parallel computing offered by a computer cluster at California State University Fullerton (CSUF) and has produced promising results in our simulated environment. The performance results of the proposed ML-AI pre-distorter using MATLAB DDPG algorithm are compared with the ideal pre-distorter for various HPA input back-off power (IPBO).
This paper presents a proportional–integral–derivative (PID)-based automatic gain control (AGC) approach for satellite communications attacked by partial-time partial-band additive white Gaussian noise (AWGN) jamming. The analysis based on the stochastic model predictive control (SMPC) shows that the AGC performance depends on the accurate characterization of the jammed signal in the future time instants. However, such characterization is generally unavailable. To overcome the limitations of the existing AGC schemes without considering the future trend of the signal amplitude tracking errors (i.e., the difference between the average amplitude and the desired amplitude), the proposed approach uses the derivative term of signal amplitude tracking errors for anticipatory control and the integral term in the PID control to eliminate steady-state errors. Furthermore, different block sizes of the sampled signals are used for computing and selecting gain control values to achieve a good trade-off between fast response and robustness to noise/jamming. Extensive simulations of a system based on the typical satellite transponder link using Quadrature Phase Shift Keying (QPSK) modulated input signals and AWGN noise/jamming demonstrate that the proposed approach can achieve better control performance for maintaining the desired signal amplitude range and smaller bite error rate (BER) in the case of AWGN jamming, as compared with the existing AGC schemes.
KEYWORDS: Sensors, Data modeling, Data fusion, Information fusion, Artificial intelligence, Systems modeling, Machine learning, Data processing, Radar, Statistical analysis
The data fusion information group (DFIG) model is widely popular, extending and replacing the joint director of the labs (JDL) model as a data fusion processing framework that considers data/information exchange, user/team involvement, and mission/task design. The DFIG/JDL provides an initial design from which enhancements in analytics, learning, and teaming result in opportunities to improve data fusion methodologies. This paper highlights recent artificial intelligence/machine learning (AI/ML), deep learning, reinforcement learning, and active learning capabilities with that of the DFIG model for analysis and systems engineering designs. The general DFIG construct is applicable to many AI/ML systems; however, the focus of the paper provides useful considerations for the data fusion community to consider based on prior implemented approaches. The main ideas are: level 0 DFIG data preprocessing through AI/ML methods for data reduction, level 1/2/3 DFIG object/situation/impact assessment using AI/ML/DL methods for awareness, level 4 DFIG process refinement with reinforcement learning for control, and level 5/6 DFIG user/mission refinement with active learning for human-machine teaming.
There are many space object tracking and identification techniques developed which require an ontology that aligns the operator with space situational awareness (SSA), space weather, and space communications. When considering space object detection, recognition, and classification; preliminary supporting information of the user needs, space environment, and signals bandwidth contribute to space traffic management. The paper discussion seeks to motivate an alignment between space weather and SSA ontologies by presenting notional ideas for developing a space domain awareness (SDA) ontology as a holistic approach that brings together various space community activities and disciplines. Such a SDA ontology is needed to be prepared for the congestion of commercial satellites, proliferation of space debris, and communications link budget analysis.
Space superiority includes space protection and space situational awareness (SSA), which require rapid and accurate space object behavioral motion and operational intent discovery. The presence of clutter, in addition to real-time and hidden information constraints, greatly complicates the space awareness decision-making to control both ground-based and space-based surveillance assets. Space is considered as an important concern in modern frontiers because intelligence information from the space has become extremely vital for strategic decisions, which calls for persistent Space Domain Awareness (SDA). The presence of disagreeable actors in addition to real-time and hidden information constraints greatly complicates the decision-making process in satellite behavior detection as well as operational intent discovery. This paper designs and implements 3D-Convoltional Neural Networks (CNNs) for rapid discovery of evasive satellite behaviors from ground-based sensors, which measure the ranges, azimuth angles, and elevation angles in the Adaptive Markov Inference Game Optimization (AMIGO) tool. The novel 3D CNN extends the generic 2d CNN towards analysis from many perspectives. To generate the 3D CNN model, the training and validation data are simulated based on our game theoretic reasoning engine for elusive space behaviors detection, interactive adversary awareness, and intelligent probing. The performance of the 3D CNN is compared with the 2D CNN models from previous work which is shown for a 10% increase in accuracy.
Various techniques, applications, and tools for space situational awareness (SSA) have been developed for specific functions that can provide decision support tools. The generality of tools to enable a user-defined operating picture (UDOP) enables analysis across a wide variety of applications. This paper explores the Multisource AI Scorecard Table (MAST) for artificial intelligence/machine learning methods. Using the MAST categories, the Adaptive Markov Inference Game Optimization (AMIGO) SSA tool is presented as an example. The analysis reveals the importance of human interaction in the task, user, and technology operations. Recent advances in artificial intelligence (AI) have led to an explosion of multimedia applications (e.g., computer vision (CV) and natural language processing (NLP)) for different domains such as commercial, industrial, and intelligence. In particular, the use of AI applications is often problematic because the opaque nature of most systems leads to an inability for a human to understand how the results came about. A reliance on “black boxes” to generate predictions and inform decisions but requires explainability. This paper explores how MAST can support human-machine interactions to support the design and development of SSA tools. After describing the elements of MAST, the use case for AMIGO explains the general rating concept for the community to consider and modify for the interpretability of advanced data analytics that support various elements of data awareness.
The paper highlights the need for methods of analytical science for multi-domain autonomy evaluation. Multidomain autonomous systems need to collect large amounts of data to verify, validate, test, and evaluate system operations. For multi-domain and uncertain scenarios, data sampling may not be adequate to fully explore and represent the entire trade space for verification and validation (V&V). However, leveraging methods from test and evaluation (T&E), a hierarchy of analytics can be developed so as to narrow the trade space. Issues in V&V/T&E employ statistics, but could benefit from first-principles physics theoretical analytics, data augmentation, and scenario design. The use of modeling is not new; however, as analytics of artificial intelligence and machine learning (AI/ML) are designed to exploit data; then there are opportunities to allow one domain (e.g., air) support data analytics in another domain (e.g., cyber).
Space superiority includes space protection and space situational awareness (SSA), which require rapid and accurate space object behavioral motion and operational intent discovery. The presence of clutter in addition to real-time and hidden information constraints greatly complicates the space awareness decision-making to control both ground-based and spacebased surveillance assets. To increase SSA, generative adversarial networks (GANs) are realizable for rapid discovery of evasive satellite behaviors. Although GANs have shown good results in synthesizing real-world images, GANs “remain remarkably difficult to train” and “approaches to attacking this problem still rely on heuristics that are extremely sensitive to modifications”. This paper describes a modification to a game-theoretic approach to incorporate GANs and train the networks using a general sum game theory. The enhanced GAN model for satellite behavior discovery is called Space unveiled Behavior GAN (SuB-GAN) in this paper. The structure includes training the GANs as a repeated game using a Fictious play concept framework, within which the discriminator (resp. generator) is updated according to the best response to the mixture outputs from a sequence of previously trained. In particular, the discriminator outputs converge to the optimum discriminator function and the mixed output from the sequence of trained generators converges to the data distribution. The simulated training datasets are used to demonstrate the enhanced GANs in the SSA domain. The performance the SuB-GAN is compared with the convolutional neural network (CNN) models showing promising results.
KEYWORDS: Data modeling, Avionic systems, Computer security, Network security, Sensors, Systems modeling, Control systems, Information fusion, Space operations, Computer architecture
Advancement in artificial intelligence (AI) and machine learning (ML), dynamic data driven application systems (DDDAS), and hierarchical cloud-fog-edge computing paradigm provide opportunities for enhancing multi-domain systems performance. As one example that represents multi-domain scenario, a “fly-by-feel” system utilizes DDDAS framework to support autonomous operations and improve maneuverability, safety and fuel efficiency. The DDDAS “fly-by-feel" avionics system can enhance multi-domain coordination to support domain specific operations. However, conventional enabling technologies rely on a centralized manner for data aggregation, sharing and security policy enforcement, and it incurs critical issues related to bottleneck of performance, data provenance and consistency. Inspired by the containerized microservices and blockchain technology, this paper introduces BLEM, a hybrid BLockchain-Enabled secure Microservices fabric to support decentralized, secure and efficient data fusion
In this paper, a Cognitive Radio Network (CRN) based on artificial intelligence is proposed to automatically classify signal modulation more efficiently, which can further help in radio frequency modeling and pattern recognition problem solving. Three different approaches Deep Neural Network (DNN), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) have been deployed and evaluated in the signal modulation classification. In this paper, the signals for training and validation are generated using our MATLAB based RF signal generator, which can simulate various types of modulated signal according to the configuration specification. The numerical results show that CNN network can outperform the DNN and RNN in terms of the signal modulation classification accuracy.
Space superiority requires space protection and space situational awareness (SSA), which rely on rapid and accurate space object behavioral and operational intent discovery. The presence of adversaries in addition to real-time and hidden information constraints greatly complicates the decision-making process in controlling both ground-based and spacebased surveillance assets. This paper develops and implements a solution called Adaptive Markov Inference Game Optimization (AMIGO) for rapid discovery of satellite behaviors. AMIGO is an adaptive feedback game theoretic approach. AMIGO gets information from sensors about the relations between the resident space objects (RSOs) of interest and ground and space surveillance assets (GSAs). The relations are determined by both the RSOs and GSAs. Therefore, AMIGO represents the situation as a game instead of a control problem. The game reasoning utilizes data level fusion, stochastic modeling/propagation, and RSO detection/tracking to predict the future RSOs-GSAs relations. The game engine also supports optional space pattern dictionary/semantic rules for adaptive transition matrices in the Markov game. If no existing pattern dictionary is available, AMIGO builds an initial one and revises it during the game reasoning. The outputs of the AMIGO reasoning include two kinds of control methods: processing of GSA measurements and localization of RSOs. The two sets form a game equilibrium, one for surveillance asset management and the other for the estimation of RSO behaviors. Numerical simulations and visualizations demonstrate the performance of AMIGO.
In many military and law-enforcement covert missions, wireless communication links need to remain undetected. In this paper, a novel spectrum spread technique based on noise modulated (NM) transmission is proposed and a feasibility study was conducted. In NM transmissions a reference pseudo-noise signal is generated and is superposed with the information signal, with a time delay. The pseudo-random noise sequence is transmitted separately and used to recover the signal from the noise modulated information signal. The transmitted information signal is noise like making it difficult to detect or decode by an adversary. If an adversary does discover the transmission, decoding is difficult without the pseudo-random noise sequence and time delay between noise and signal. Conventional NM uses polarized antennas to orthogonally transmit the noise modulated information signal and pseudo-noise signal. However, the two polarized antennas are rarely, if ever, completely isolated in practice making signal recovery difficult if not impossible. In this paper a feasibility study was performed on a novel multi-frequency NM scheme for NM communications with a single polarized antenna. A universal software radio peripheral (USRP) software defined radio (SDR) testbed was used to demonstrate that multi-frequency NM transmission masks a QPSK signal from an adversary and the signal can be successfully recovered by a friendly receiver.
KEYWORDS: Satellites, Data modeling, Information fusion, Sensors, Satellite communications, Meteorological satellites, Systems modeling, Satellite navigation systems, Control systems, Environmental sensing
Advancements in artificial intelligence, information communication, and systems design are potential for autonomous systems emerging for space situation awareness (SSA) architectures. Examples of architecture designs are autonomy in motion (AIM) for dynamic data assessment systems (e.g., robotics) and autonomy at rest (AAR) for static data collection systems (e.g., surveillance). However, there is a need for data architectures which are tailored to the SSA missions, which necessitates autonomy in use (AIU). AIU requires pragmatic use of message passing and data flow architectures, contextual and theoretic modeling, and user and information fusion. Information fusion provides methods for data aggregation, correlation, and temporal assessment and awareness. Together, AIU accesses the dynamic data for autonomy in change (AIC), information fusion from AAR in order to make AIM real-time decisions. The paper discusses issues for space situation awareness directions focusing on autonomy in use.
In satellite communication (SATCOM) system, a simple “bent-pipe” transponder is widely adopted to convert uplink carrier frequencies to downlink carrier frequencies for transmission of information without having on-board processing capability. The transponders are equipped with high power amplifiers (HPAs), which like other amplifier modules in communication systems, cause nonlinear distortions to transmitted signals, when HPAs are operated at or close to their saturation points to maximize power efficiency. These nonlinearities can be characterized as amplitude modulation-toamplitude modulation (AM-AM), and amplitude modulation-to-phase modulation (AM-PM) effects, which degrade the transmission performance of the system. Therefore, additional processing techniques such as predistortion (PD) has applied to maximize the transponder throughput along with the HPA power efficiency. In this paper, we first propose an accurate HPA modelling method, which leads to an outstanding agreement with the measured HPA AM-AM and AM-PM characteristics data. Then, a close-form PD is derived with respect to the power and phase compensation for the corresponding output signals of HPA. Finally, simulation results are provided to evaluate and verify the bit error rate (BER) improvement for the considered SATCOM system by applying our proposed PD technique.
We propose the diffusion-based enhanced covariance intersection cooperative space object tracking (DeCiSpOT) filter. The main advantage of the proposed DeCiSpOT algorithm is that it can balance the computational complexity and communication requirements between different sensors as well as improve track accuracy when measurements do not exist or are of low accuracy. Instead of using the standard covariance intersection in the diffusion step, the enhanced diffusion strategy integrates the 0-1 weighting covariance intersection strategy and the iterative covariance intersection strategy. The proposed DeCiSpOT algorithm also uses the global nearest neighbor and probabilistic data association for multiple space object tracking. Two typical scenarios including cooperative single and multiple space object tracking are used to demonstrate the performance of the proposed DeCiSpOT filter. Using simulated ground-based electro-optical (EO) measurements for multiple resident space objects and multiple distributed EO sensors, the DeCiSpOT archived results comparable to an optimal centralized approach. The results demonstrate that the DeCiSpOT is effective for space object tracking problem with results close to the optimal centralized cubature Kalman filter.
This paper revises and evaluates an orbital emulator (OE) for space situational awareness (SSA). The OE can produce 3D satellite movements using capabilities generated from omni-wheeled robot and robotic arm motions. The 3D motion of satellite is partitioned into the movements in the equatorial plane and the up-down motions in the vertical plane. The 3D actions are emulated by omni-wheeled robot models while the up-down motions are performed by a stepped-motorcontrolled- ball along a rod (robotic arm), which is attached to the robot. Lidar only measurements are used to estimate the pose information of the multiple robots. SLAM (simultaneous localization and mapping) is running on one robot to generate the map and compute the pose for the robot. Based on the SLAM map maintained by the robot, the other robots run the adaptive Monte Carlo localization (AMCL) method to estimate their poses. The controller is designed to guide the robot to follow a given orbit. The controllability is analyzed by using a feedback linearization method. Experiments are conducted to show the convergence of AMCL and the orbit tracking performance.
In this paper, the dynamic enhanced cubature Kalman filter is proposed to explore the constraint of the long-term relationship of system states. The performance of the proposed dynamic enhanced cubature Kalman filter (DECKF) is compared to the conventional cubature Kalman filter via two numerical examples. The simulation results show that the proposed filter can provide better performance than conventional cubature Kalman filter, for certain scenarios.
Due to the progressive expansion of public mobile networks and the dramatic growth of the number of wireless users in recent years, researchers are motivated to study the radio propagation in urban environments and develop reliable and fast path loss prediction models. During last decades, different types of propagation models are developed for urban scenario path loss predictions such as the Hata model and the COST 231 model. In this paper, the path loss prediction model is thoroughly investigated using machine learning approaches. Different non-linear feature selection methods are deployed and investigated to reduce the computational complexity. The simulation results are provided to demonstratethe validity of the machine learning based path loss prediction engine, which can correctly determine the signal propagation in a wireless urban setting.
The dynamic data-driven applications systems (DDDAS) paradigm is meant to inject measurements into the execution model for enhanced systems performance. One area off interest in DDDAS is for space situation awareness (SSA). For SSA, data is collected about the space environment to determine object motions, environments, and model updates. Dynamically coupling between the data and models enhances the capabilities of each system by complementing models with data for system control, execution, and sensor management. The paper overviews some of the recent developments in SSA made possible from DDDAS techniques which are for object detection, resident space object tracking, atmospheric models for enhanced sensing, cyber protection, and information management.
This paper develops and evaluates an orbital emulator (OE) for space situational awareness (SSA). The OE can produce 3D satellite movements using capabilities generated from omni-wheeled robot and robotic arm motion methods. The 3D motion of a satellite is partitioned into the movements in the equatorial plane and the up-down motions in the vertical plane. The 3D actions are emulated by omni-wheeled robot models while the up-down motions are performed by a stepped-motor-controlled-ball along a rod (robotic arm), which is attached to the robot. For multiple satellites, a fast map-merging algorithm is integrated into the robot operating system (ROS) and simultaneous localization and mapping (SLAM) routines to locate the multiple robots in the scene. The OE is used to demonstrate a pursuit-evasion (PE) game theoretic sensor management algorithm, which models conflicts between a space-based-visible (SBV) satellite (as pursuer) and a geosynchronous (GEO) satellite (as evader). The cost function of the PE game is based on the informational entropy of the SBV-tracking-GEO scenario. GEO can maneuver using a continuous and low thruster. The hard-in-loop space emulator visually illustrates the SSA problem solution based PE game.
Typical surveillance systems employ decision- or feature-level fusion approaches to integrate heterogeneous sensor data, which are sub-optimal and incur information loss. In this paper, we investigate data-level heterogeneous sensor fusion. Since the sensors monitor the common targets of interest, whose states can be determined by only a few parameters, it is reasonable to assume that the measurement domain has a low intrinsic dimensionality. For heterogeneous sensor data, we develop a joint-sparse data-level fusion (JSDLF) approach based on the emerging joint sparse signal recovery techniques by discretizing the target state space. This approach is applied to fuse signals from multiple distributed radio frequency (RF) signal sensors and a video camera for joint target detection and state estimation. The JSDLF approach is data-driven and requires minimum prior information, since there is no need to know the time-varying RF signal amplitudes, or the image intensity of the targets. It can handle non-linearity in the sensor data due to state space discretization and the use of frequency/pixel selection matrices. Furthermore, for a multi-target case with J targets, the JSDLF approach only requires discretization in a single-target state space, instead of discretization in a J-target state space, as in the case of the generalized likelihood ratio test (GLRT) or the maximum likelihood estimator (MLE). Numerical examples are provided to demonstrate that the proposed JSDLF approach achieves excellent performance with near real-time accurate target position and velocity estimates.
For the short-arc angle only orbit initialization problem, the admissible area is often used. However, the accuracy using a single sensor is often limited. For high value space objects, it is desired to achieve more accurate results. Fortunately, multiple sensors, which are dedicated to space situational awareness, are available. The work in this paper uses multiple sensors’ information to cooperatively initialize the orbit based on the fusion of multiple admissible areas. Both the centralized fusion and decentralized fusion are discussed. Simulation results verify the expectation that the orbit initialization accuracy is improved by using information from multiple sensors.
In this paper, a Cognitive Radio Network (CRN) based on artificial intelligence is proposed to distribute the limited radio spectrum resources more efficiently. The CRN framework can analyze the time-sensitive signal data close to the signal source using fog computing with different types of machine learning techniques. Depending on the computational capabilities of the fog nodes, different features and machine learning techniques are chosen to optimize spectrum allocation. Also, the computing nodes send the periodic signal summary which is much smaller than the original signal to the cloud so that the overall system spectrum source allocation strategies are dynamically updated. Applying fog computing, the system is more adaptive to the local environment and robust to spectrum changes. As most of the signal data is processed at the fog level, it further strengthens the system security by reducing the communication burden of the communications network.
KEYWORDS: Computer security, Information security, Network security, Defense and security, Web services, Data fusion, Defense systems, Sensors, Data modeling, Systems modeling
With the explosive growth of network technologies, insider attacks have become a major concern to business operations that largely rely on computer networks. To better detect insider attacks that marginally manipulate network traffic over time, and to recover the system from attacks, in this paper we implement a temporal-based detection scheme using the sequential hypothesis testing technique. Two hypothetical states are considered: the null hypothesis that the collected information is from benign historical traffic and the alternative hypothesis that the network is under attack. The objective of such a detection scheme is to recognize the change within the shortest time by comparing the two defined hypotheses. In addition, once the attack is detected, a server migration-based system recovery scheme can be triggered to recover the system to the state prior to the attack. To understand mitigation of insider attacks, a multi-functional web display of the detection analysis was developed for real-time analytic. Experiments using real-world traffic traces evaluate the effectiveness of Detection System and Recovery (DeSyAR) scheme. The evaluation data validates the detection scheme based on sequential hypothesis testing and the server migration-based system recovery scheme can perform well in effectively detecting insider attacks and recovering the system under attack.
KEYWORDS: Signal to noise ratio, Transmitters, Radar, Receivers, Antennas, Signal detection, Target detection, Radar, Sensors, Error analysis, Radar signal processing
This paper presents a low size, weight and power – cost (SWaP-C) airborne sense and avoid (ABSAA) system, which is based on a linear frequency modulated continuous wave (LFMCW) radar and can be mounted on small unmanned aircraft system (UAS). The system satisfies the constraint of the available sources on group 2/3 UAS. To obtain the desired sense and avoid range, a narrow band frequency (or range) scanning technique is applied for reducing the receiver’s noise floor to improve its sensitivity, and a digital signal integration with fast Fourier transform (FFT) is applied to enhance the signal to noise ratio (SNR). The gate length and chirp rate are intelligently adapted to not only accommodate different object distances, speeds and approaching angle conditions, but also optimize the detection speed, resolution and coverage range. To minimize the radar blind zone, a higher chirp rate and a narrowband intermediate frequency (IF) filter are applied at the near region with a single antenna signal for target detection. The offset IF frequency between transmitter (TX) and receiver (RX) is designed to mitigate the TX leakage to the receiver, especially at close distances. Adaptive antenna gain and beam-width are utilized for searching at far distance and fast 360 degree middle range. For speeding up the system update rate, lower chirp rates and wider IF and baseband filters are applied for obtaining larger range scanning step length out of the near region. To make the system working with a low power transmitter (TX), multiple-antenna beamforming, digital signal integration with FFT, and a much narrower receiver (RX) bandwidth are applied at the far region. The ABSAA system working range is 2 miles with a 1W transmitter and single antenna signal detection, and it is 5 miles when a 5W transmitter and 4-antenna beamforming (BF) are applied.
Satellite systems including the Global Navigation Satellite System (GNSS) and the satellite communications (SATCOM) system provide great convenience and utility to human life including emergency response, wide area efficient communications, and effective transportation. Elements of satellite systems incorporate technologies such as navigation with the global positioning system (GPS), satellite digital video broadcasting, and information transmission with a very small aperture terminal (VSAT), etc. The satellite systems importance is growing in prominence with end users’ requirement for globally high data rate transmissions; the cost reduction of launching satellites; development of smaller sized satellites including cubesat, nanosat, picosat, and femtosat; and integrating internet services with satellite networks. However, with the promising benefits, challenges remain to fully develop secure and robust satellite systems with pervasive computing and communications. In this paper, we investigate both cyber security and radio frequency (RF) interferences mitigation for satellite systems, and demonstrate that they are not isolated. The action space for both cyber security and RF interferences are firstly summarized for satellite systems, based on which the mitigation schemes for both cyber security and RF interferences are given. A multi-layered satellite systems structure is provided with cross-layer design considering multi-path routing and channel coding, to provide great security and diversity gains for secure and robust satellite systems.
In this paper we consider a problem of estimating the signal-to-interference-plus-noise ratio (SINR) for satellite transmission system in the presence of jamming signals. Additive white Gaussian noise (AWGN) channels are considered for baseband quadrature phase shift keying (QPSK) data transmission system. Two interference models are proposed with Gaussian or non-Gaussian interference signals in order to investigate the SINR for different satellite transmission jamming scenarios. Both non-data-aided moment-based and data-aided maxi-mum likelihood SINR estimators are derived for the systems. The normalized mean square errors of the SINR estimation algorithms are examined by means of computer simulations. The numerical results show the robust-ness of derived SINR estimators. The development of the SINR estimators are applicable to a large number of applications utilizing satellite communication systems.
This paper develops and evaluates a satellite orbital testbed (SOT) for satellite communications (SATCOM). SOT can emulate the 3D satellite orbit using the omni-wheeled robots and a robotic arm. The 3D motion of satellite is partitioned into the movements in the equatorial plane and the up-down motions in the vertical plane. The former actions are emulated by omni-wheeled robots while the up-down motions are performed by a stepped-motor-controlled-ball along a rod (robotic arm), which is attached to the robot. The emulated satellite positions will go to the measure model, whose results will be used to perform multiple space object tracking. Then the tracking results will go to the maneuver detection and collision alert. The satellite maneuver commands will be translated to robots commands and robotic arm commands. In SATCOM, the effects of jamming depend on the range and angles of the positions of satellite transponder relative to the jamming satellite. We extend the SOT to include USRP transceivers. In the extended SOT, the relative ranges and angles are implemented using omni-wheeled robots and robotic arms.
KEYWORDS: Satellite communications, Meteorology, Signal attenuation, Ka band, X band, Receivers, Satellites, Signal to noise ratio, Antennas, Systems modeling
This paper investigates weather effects on a satellite communication (SATCOM) link communication channel model. Specifically, rain attenuation in the Ka band and X band of the SATCOM link for both uplink and downlink scenarios are presented. The weather model for the SATCOM link uses a Markov chain model with an average probability and transition probability for different states of weather, to investigate the impact of dynamic weather on the SATCOM link channel propagation model. Also, a power control method is proposed to achieve the required carrier to noise ratio in a SATCOM scenario using a Bayesian Network in Netica. The Bayesian Network models the space-ground link geometry and transmit power control to adapt to the dynamic weather. Simulations are implemented for the weather states during relatively long and short periods, path loss variations, and transmit power distributions over different scenarios. The simulation results demonstrate the effectiveness of the proposed weather model, Markov chain model, and the power control method for SATCOM.
KEYWORDS: Control systems, Satellites, Ions, Solar radiation models, Space operations, Analytical research, Systems modeling, Motion models, Sensors, Neodymium
This paper develops and evaluates a pursuit-evasion (PE) game approach for elusive orbital maneuver and space object tracking. Unlike the PE games in the literature, where the assumption is that either both players have perfect knowledge of the opponents’ positions or use primitive sensing models, the proposed PE approach solves the realistic space situation awareness (SSA) problem with imperfect information, where the evaders will exploit the pursuers’ sensing and tracking models to confuse their opponents by maneuvering their orbits to increase the uncertainties, which the pursuers perform orbital maneuvers to minimize. In the game setup, each game player P (pursuer) and E (evader) has its own motion equations with a small continuous low-thrust. The magnitude of the low thrust is fixed and the direction can be controlled by the associated game player. The entropic uncertainty is used to generate the cost functions of game players. The Nash or mixed Nash equilibrium is composed of the directional controls of low-thrusts. Numerical simulations are emulated to demonstrate the performance. Simplified perturbations models (SGP4/SDP4) are exploited to calculate the ground truth of the satellite states (position and speed).
KEYWORDS: Sensors, Defense systems, Sensor networks, Network security, Databases, Information security, Discrete wavelet transforms, Active sensors, Digital signal processing, Defense and security
In this paper, an implemented defense system is demonstrated to carry out cyber security situation awareness. The developed system consists of distributed passive and active network sensors designed to effectively capture suspicious information associated with cyber threats, effective detection schemes to accurately distinguish attacks, and network actors to rapidly mitigate attacks. Based on the collected data from network sensors, image-based and signals-based detection schemes are implemented to detect attacks. To further mitigate attacks, deployed dynamic firewalls on hosts dynamically update detection information reported from the detection schemes and block attacks. The experimental results show the effectiveness of the proposed system. A future plan to design an effective defense system is also discussed based on system theory.
This paper presents a time division multiple access (TDMA) multiple-input and multiple-output (MIMO) synthetic aperture radar (SAR) with a sliding range window for automated position-keeping, which can be applied in vessel tracking/escorting, offshore deepwater drillship equipment servicing, etc. A MIMO SAR sensor predefines a special part of the target (i.e., the drillship, ship, or submarine) as the measurement target and does not need special assistant devices/targets installed on the target vessel/platform, so its application is convenient. In the measurement process, the sensor scans the target with multiple ranging gates, forms images of multiple sections of the target, detects the predefined part/target in these images, and then obtains the range and angle of the predefined target for relative localization. Our MIMO SAR has 13 transmitting antennas and 8 receiving antennas. All transmitting antennas share a transmitter and all receiving antennas share a receiver using switches to reduce cost. The MIMO SAR radar has 44 effective SAR phase centers, and the azimuth angle resolution is θ0.5/44 (finest, θ 0.5 is the antenna element’s 3dB beamwidth). The transmitter transmits a chirped linear frequency modulated continuous wave (LFMCW) signal, and the receiver only processes the signal limited in the beat frequency region defined by the distance from the measurement target to the sensor and the interested measurement target extension, which is determined by the receiver bandwidth. With the sliding range window, the sensor covers a large range, and in the covered range window, it provides high accuracy measurements.
Game theory is a useful method to model interactions between agents with conflicting interests. In this paper, we set up a Game Theoretic Model for Satellite Communications (SATCOM) to solve the interaction between the transmission pair (blue side) and the jammer (red side) to reach a Nash Equilibrium (NE). First, the IFT Game Application Model (iGAM) for SATCOM is formulated to improve the utility of the transmission pair while considering the interference from a jammer. Specifically, in our framework, the frame error rate performance of different modulation and coding schemes is used in the game theoretic solution. Next, the game theoretic analysis shows that the transmission pair can choose the optimal waveform and power given the received power from the jammer. We also describe how the jammer chooses the optimal power given the waveform and power allocation from the transmission pair. Finally, simulations are implemented for the iGAM and the simulation results show the effectiveness of the SATCOM power allocation, waveform selection scheme, and jamming mitigation.
Radio frequency (RF) wireless communication is reaching its capacity to support large data rate transmissions due to hardware constraints (e.g., silicon processes), software strategies (e.g., information theory), and consumer desire for timely large file exchanges (e.g., big data and mobile cloud computing). A high transmission rate performance must keep pace with the generated huge volumes of data for real-time processing. Integrated RF and optical wireless communications (RF/OWC) could be the next generation transmission technology to satisfy both the increased data rate exchange and the communications constraints. However, with the promising benefits of RF/OWC, challenges remain to fully develop hybrid RF with wireless optical communications such as uniform waveform design for information transmission and detection. In this paper, an orthogonal frequency division multiplexing (OFDM) transmission scheme, which widely employed in RF communications, is developed for optical communications. The traditional high peak-to-average power ratio (PAPR) in OFDM is reduced to improve system performance. The proposed multi-carrier waveform is evaluated with a frequency-selective fading channel. The results demonstrate that bit error rate (BER) performance of our proposed optical OFDM transmission technique outperforms the traditional OWC on-off keying (OOK) transmission scheme.
Game theoretical methods have been used for spectral awareness, space situational awareness (SSA), cyber situational awareness (CSA), and Intelligence, Surveillance, and Reconnaissance situation awareness (ISA). Each of these cases, awareness is supported by sensor estimation for assessment and the situation is determined from the actions of multiple players. Game theory assumes rational actors in a defined scenario; however, variations in social, cultural and behavioral factors include the dynamic nature of the context. In a dynamic data-driven application system (DDDAS), modeling must include both the measurements but also how models are used by different actors with different priorities. In this paper, we highlight the applications of game theory by reviewing the literature to determine the current state of the art and future needs. Future developments would include building towards knowledge awareness with information technology (e.g., data aggregation, access, indexing); multiscale analysis (e.g., space, time, and frequency), and software methods (e.g., architectures, cloud computing, protocols).
KEYWORDS: Sensors, Defense and security, Sensor networks, Defense systems, Data centers, Signal processing, Analytical research, Network security, Signal detection, Detection and tracking algorithms
Network sensor-based defense (NSD) systems have been widely used to defend against cyber threats. Nonetheless, if the adversary finds ways to identify the location of monitor sensors, the effectiveness of NSD systems can be reduced. In this paper, we propose both temporal and spatial perturbation based defense mechanisms to secure NSD systems and make the monitor sensor invisible to the adversary. The temporal-perturbation based defense manipulates the timing information of published data so that the probability of successfully recognizing monitor sensors can be reduced. The spatial-perturbation based defense dynamically redeploys monitor sensors in the network so that the adversary cannot obtain the complete information to recognize all of the monitor sensors. We carried out experiments using real-world traffic traces to evaluate the effectiveness of our proposed defense mechanisms. Our data shows that our proposed defense mechanisms can reduce the attack accuracy of recognizing detection sensors.
Space situational awareness (SSA) is critical to many space missions serving weather analysis, communications, and navigation. However, the number of sensors used in space situational awareness is limited which hinders collision avoidance prediction, debris assessment, and efficient routing. Hence, it is critical to use such sensor resources efficiently. In addition, it is desired to develop the SSA sensor management algorithm in a distributed manner. In this paper, a distributed sensor management approach using the negotiation game (NG-DSM) is proposed for the SSA. Specifically, the proposed negotiation game is played by each sensor and its neighboring sensors. The bargaining strategies are developed for each sensor based on negotiating for accurately tracking desired targets (e.g., satellite, debris, etc.) . The proposed NG-DSM method is tested in a scenario which includes eight space objects and three different sensor modalities which include a space based optical sensor, a ground radar, or a ground Electro-Optic sensor. The geometric relation between the sensor, the Sun, and the space object is also considered. The simulation results demonstrate the effectiveness of the proposed NG-DSM sensor management methods, which facilitates an application of multiple-sensor multiple-target tracking for space situational awareness.
KEYWORDS: Video, Video surveillance, Surveillance, Information fusion, Information security, Unmanned aerial vehicles, Sensors, Semantic video, Data fusion, Process modeling
Future surveillance systems will work in complex and cluttered environments which require systems engineering
solutions for such applications such as airport ground surface management. In this paper, we highlight the use of a L1
video tracker for monitoring activities at an airport. We present methods of information fusion, entity detection, and
activity analysis using airport videos for runway detection and airport terminal events. For coordinated airport security,
automated ground surveillance enhances efficient and safe maneuvers for aircraft, unmanned air vehicles (UAVs) and
unmanned ground vehicles (UGVs) operating within airport environments.
Most enterprise networks are built to operate in a static configuration (e.g., static software stacks, network configurations, and application deployments). Nonetheless, static systems make it easy for a cyber adversary to plan and launch successful attacks. To address static vulnerability, moving target defense (MTD) has been proposed to increase the difficulty for the adversary to launch successful attacks. In this paper, we first present a literature review of existing MTD techniques. We then propose a generic defense framework, which can provision an incentive-compatible MTD mechanism through dynamically migrating server locations. We also present a user-server mapping mechanism, which not only improves system resiliency, but also ensures network performance. We demonstrate a MTD with a multi-user network communication and our data shows that the proposed framework can effectively improve the resiliency and agility of the system while achieving good network timeliness and throughput performance.
KEYWORDS: Quantum communications, Quantum computing, Satellites, Aerospace engineering, Sensors, Chemical species, Clocks, Global Positioning System, Space operations, Quantum key distribution
In this paper, quantum technology is introduced with three key topics, including quantum computing, quantum communication, and quantum devices. Using these dimensions of quantum techniques we briefly introduce their contributions to aerospace applications. The paper will help readers to understand the basic concepts of the quantum technology and their potential applications in space, air, and ground applications such as highly accurate target positioning.
Satellite networks and quantum communications offer complementary opportunities for enhanced operations. Quantum communications provide security for the transmissions between satellites and ground stations; while the free-space link of satellite networks provide the potential of long distance transmission of quantum bits (qubit) for space communications. However, with the promising advantages of the two approaches, challenges remain to fully develop quantum-based satellite communications such as robust and reliable information detection which is difficult to achieve due to the movement of satellites. In this paper, a tracking algorithm is proposed for polarization-encoded quantum satellite communications where polarization states are used to determine the bit transfer between the transmitter and receiver. The polarization tracking is essential for the decoding of a qubit and the quantum key distribution (QKD). A practical channel model for free-space quantum communications is adopted in this paper. With the estimated polarization, a novel dynamic polarization compensation scheme is also proposed. The results show that our methods can accurately estimate the polarization, providing much lower quantum bit error rate (QBER) by compensation, as compared with the direct qubit detection without polarization tracking and compensation scheme.
To date, Unmanned Aerial Vehicles (UAVs) have been widely used for numerous applications. UAVs can directly connect to ground stations or satellites to transfer data. Multiple UAVs can communicate and cooperate with each other and then construct an ad-hoc network. Multi-UAV systems have the potential to provide reliable and timely services for end users in addition to satellite networks. In this paper, we conduct a simulation study for evaluating the network performance of multi-UAV systems and satellite networks using the ns-2 networking simulation tool. Our simulation results show that UAV communication networks can achieve better network performance than satellite networks and with a lower cost and increased timeliness. We also investigate security resiliency of UAV networks. As a case study, we simulate false data injection attacks against UAV communication networks in ns-2 and demonstrate the impact of false data injection attacks on network performance.
Recently bio-inspired rendezvous strategies have been investigated for applications in space situation awareness.
Particularly, closed-loop solutions have been developed for the cases that the target object is in a circular orbit without
considering any orbital perturbations. In this paper, the minimum-fuel consumption bio-inspired motions are further
studied. The follow cases considering the J2 perturbation, the non-zero eccentricities, and different boundary conditions
are analyzed: (1) the target object is at the local vertical local horizontal coordinate origin; (2) the target is moving in the
local vertical local horizontal coordinate; (3) the rendezvous object approaches the target object from the R-bar, V-bar,
and Z-bar directions, respectively. Fast solutions can be obtained for the rendezvous object to approach the target object
with minimum energy consumption.
Space situational awareness (SSA) and defense space control capabilities are top priorities for groups that own or operate man-made spacecraft. Also, with the growing amount of space debris, there is an increase in demand for contextual understanding that necessitates the capability of collecting and processing a vast amount sensor data. Cloud computing, which features scalable and flexible storage and computing services, has been recognized as an ideal candidate that can meet the large data contextual challenges as needed by SSA. Cloud computing consists of physical service providers and middleware virtual machines together with infrastructure, platform, and software as service (IaaS, PaaS, SaaS) models. However, the typical Virtual Machine (VM) abstraction is on a per operating systems basis, which is at too low-level and limits the flexibility of a mission application architecture. In responding to this technical challenge, a novel adaptive process based cloud infrastructure for SSA applications is proposed in this paper. In addition, the details for the design rationale and a prototype is further examined. The SSA Cloud (SSAC) conceptual capability will potentially support space situation monitoring and tracking, object identification, and threat assessment. Lastly, the benefits of a more granular and flexible cloud computing resources allocation are illustrated for data processing and implementation considerations within a representative SSA system environment. We show that the container-based virtualization performs better than hypervisor-based virtualization technology in an SSA scenario.
Detecting and identifying targets in unmanned aerial vehicle (UAV) images and videos have been challenging problems due to various types of image distortion. Moreover, the significantly high processing overhead of existing image/video processing techniques and the limited computing resources available on UAVs force most of the processing tasks to be performed by the ground control station (GCS) in an off-line manner. In order to achieve fast and autonomous target identification on UAVs, it is thus imperative to investigate novel processing paradigms that can fulfill the real-time processing requirements, while fitting the size, weight, and power (SWaP) constrained environment. In this paper, we present a new autonomous target identification approach on UAVs, leveraging the emerging neuromorphic hardware which is capable of massively parallel pattern recognition processing and demands only a limited level of power consumption. A proof-of-concept prototype was developed based on a micro-UAV platform (Parrot AR Drone) and the CogniMemTMneural network chip, for processing the video data acquired from a UAV camera on the y. The aim of this study was to demonstrate the feasibility and potential of incorporating emerging neuromorphic hardware into next-generation UAVs and their superior performance and power advantages towards the real-time, autonomous target tracking.
Moving vehicle detection in wide area motion imagery is a challenging task due to the large motion of the camera and
the small number of pixels on the target. At the same time, this task is very important for surveillance applications, and
the result can be used for urban traffic management, accident and emergency responder routing. Also, the effectiveness of
the context in object detection task can be further explored to increase target tracking accuracy. In this paper, we propose
to use Spatial Context(SC) to improve the performance of the vehicle detection task. We first model the background
of 8 consecutive frames with median filter, and get candidates by using background subtraction. The SC is built based
on the candidates that have been classified as positive by Histograms of Oriented Gradient(HOG) with Multiple Kernel
Learning(MKL). The region around each positive candidate is divided into m subregions with a fixed length l, then, the
SC, a histogram, is built based on the number of positive candidates in each region. We use the publicly available CLIF
2006 dataset to evaluate the effect of SC. The experiments demonstrate that SC is useful to remove false positives, around
which there are few positive candidates, and the combination of SC and HOG with multiple kernel learning outperforms
the use of SC or HOG only.
Traditional tracking frameworks are challenged by low video frame rate scenarios, because the appearances and locations
of the target may change considerably in consecutive frames. Our paper presents a saliency-based temporal association
dependency (STAD) framework to deal with such a low frame rate scenario and demonstrate good results in our
robot testbed. We first use median filter to create a background of the scene, then apply background subtraction to every
new frame to decide the rough position of the target. With the help of the markers on the robots, we use a gradient voting
algorithm to detect the high responses of the directions of the robots. Finally, a template matching with branch pruning
is used to obtain the finer estimation of the pose of the robots. To make the tracking-by-detection framework stable, we
further introduce the temporal constraints using a previously detected result as well as an association technique. Our experiments
show that our method can achieve a very stable tracking result and outperforms some state-of-the-art trackers such
as Meanshift, Online-AdaBoosting, Mulitple-Instance-Learning, Tracking-Learning-Detection etc. Also. we demonstrate
that our algorithm provides near real-time solutions given the low frame rate requirement.
Automatic vehicle license plate recognition (LPR) is important for intelligent traffic surveillance systems. This paper
suggests a vehicle license plate algorithm, color component texture detection and template matching (CCTD-TM).
CCTD-TM has advantages of ease of implementation and highly efficient in calculation. We suggest a novel algorithm
of color component texture for license plate localization. This algorithm takes advantage of the feature of fixed color
texture of plate base and character. The image preprocessing and character recognition by template matching parts are
included in the LPR algorithm. The preliminary results demonstrate an average detection rate over 96.5% and an average
recognition rate over 89.9% on hundreds of vehicle images tested in the experiments.
Networking technologies are exponentially increasing to meet worldwide communication requirements. The rapid
growth of network technologies and perversity of communications pose serious security issues. In this paper, we aim to
developing an integrated network defense system with situation awareness capabilities to present the useful information
for human analysts. In particular, we implement a prototypical system that includes both the distributed passive and active
network sensors and traffic visualization features, such as 1D, 2D and 3D based network traffic displays. To effectively
detect attacks, we also implement algorithms to transform real-world data of IP addresses into images and study the pattern
of attacks and use both the discrete wavelet transform (DWT) based scheme and the statistical based scheme to detect
attacks. Through an extensive simulation study, our data validate the effectiveness of our implemented defense system.
The need for a global collaborating space situational awareness (SSA) network, including radars, optical and other sensors for communication and surveillance, has become a top priority for most countries who own or operate man-made space-crafts. Such a SSA system requires vast storage, powerful computing capacity and the ability to serve hundreds of thousands of users to access the same database. These requirements make traditional distributed networking system insufficient. Cloud computing, which features scalable and elastic storage and computing services, has been recognized as an ideal candidate that can meet the challenges of SSA systems' requirements. In this paper, we propose a Cloud-based information fusion system for SSA and examine a prototype that serves space tracking algorithms. We discuss the benefits of using Cloud Computing as an alternative for data processing and storage and explore details of Cloud implementation for a representative SSA system environment.
Atmospheric clouds are commonly encountered phenomena affecting visual tracking from air-borne or space-borne
sensors. Generally clouds are difficult to detect and extract because they are complex in shape and interact with sunlight
in a complex fashion. In this paper, we propose a clustering game theoretic image segmentation based approach to
identify, extract, and patch clouds. In our framework, the first step is to decompose a given image containing clouds. The
problem of image segmentation is considered as a “clustering game”. Within this context, the notion of a cluster is
equivalent to a classical equilibrium concept from game theory, as the game equilibrium reflects both the internal and
external (e.g., two-player) cluster conditions. To obtain the evolutionary stable strategies, we explore three evolutionary
dynamics: fictitious play, replicator dynamics, and infection and immunization dynamics (InImDyn). Secondly, we use
the boundary and shape features to refine the cloud segments. This step can lower the false alarm rate. In the third step,
we remove the detected clouds and patch the empty spots by performing background recovery. We demonstrate our
cloud detection framework on a video clip provides supportive results.
In this work, a Quality of Service (QoS)-aware routing (QAR) algorithm is developed for Low-Earth
Orbit (LEO) polar constellations. LEO polar orbits are the only type of satellite constellations where
inter-plane inter-satellite links (ISLs) are implemented in real world. The QAR algorithm exploits
features of the topology of the LEO satellite constellation, which makes it more efficient than general
shortest path routing algorithms such as Dijkstra’s or extended Bellman-Ford algorithms. Traffic
density, priority, and error QoS requirements on communication delays can be easily incorporated into
the QAR algorithm through satellite distances. The QAR algorithm also supports efficient load
balancing in the satellite network by utilizing the multiple paths from the source satellite to the
destination satellite, and effectively lowers the rate of network congestion. The QAR algorithm
supports a novel robust routing scheme in LEO polar constellation, which is able to significantly
reduce the impact of inter-satellite link (ISL) congestions on QoS in terms of communication delay
and jitter.
KEYWORDS: Sensors, Telecommunications, Cognitive modeling, Signal to noise ratio, Receivers, Data communications, Transmitters, Data modeling, Control systems, Space operations
In this paper, we consider a cognitive radio based space communication system in a game-theoretical framework, where
players dynamically interact through wireless channels to utilize the wideband spectrum for their objectives. The
performance indices include data rate, covertness, jamming, and anti-jamming; each of which relate to an effective
signal-nose-ratio (SNR). The game players have different intents and asymmetric and hierarchical information about the
frequency spectrum which are modeled as three different types of players: primary users, secondary users, and hostile
active jammers. We consider the informational asymmetry in two situations: (1) different information sets for friendly
users and jammers and (2) even among the friendly sensors; some sensors may only have partial or little information
about others due to jammed observations. Such an asymmetric information pattern naturally partitions the sensors into
leaders and followers. In our hierarchical anti-jammer approach, a two level approach includes a pursuit-evasion game
and a Stackelberg game. At the higher-level, a non-cooperative pursuit-evasion game is constructed to model the
interactions between jammer and primary users in the frequency-location domains. At the lower level, primary and
secondary users play a dynamic Stackelberg game in the presence of jammers. Theoretical game solutions are provided
to demonstrate the proposed proactive jamming mitigation strategy.
KEYWORDS: Antennas, Error analysis, Receivers, Received signal strength, Global Positioning System, Control systems, Data fusion, Phase shifts, Signal to noise ratio, Analytical research
This paper presents an emitter localization technique based on the fusion of Direction of Arrival (DOA) measurements
obtained from two miniature unmanned aerial systems (UAS) and the terrain map of the interested area. The system's
objective is to localize an emitter distributed in an area with 2000m radius in real time and the localization error is less
than 100m with 95% confidence. In the system, each UAS is equipped with a three-element smart antenna for scanning
the desired frequency band, calculating the received signal's spectrum signature and estimating the emitter's elevation
and azimuth DOA. The received signal's DOA, spectrum signature, UAS position, and the time that the signal is
received (calculated with respected to the pulse per second (PPS) signal of global positioning system (GPS)) are
transmitted to the ground control station. At the ground control station, the DOA coming from the two UAS are aligned
using the received signal's spectrum signature and time stamp, and then fused with the UAS position and terrain map to
localize the emitter. This paper is focused on the localization scheme including the DOA estimation and emitter
localization based on data fusion. The simulation conducted shows that azimuth DOA error (about 1.5°) is much smaller
than elevation DOA error (about 5°), and the achieved localization error is less than 100m in most cases when the UAS
and the emitter are located in an area with radius of 2000m.
KEYWORDS: Signal to noise ratio, Receivers, Transmitters, Sensors, Signal detection, Data transmission, Interference (communication), Antennas, Detection theory, Fusion energy
In this paper a basic cognitive jamming/anti-jamming problem is studied in the context of space communication. The
scenario involves a pair of transmitter and receiver, and a cognitive jammer. The cognitive jammer is assumed to have
powerful spectrum sensing capability that allows it to detect data transmission from the transmitter to the receiver over
the communication channels. Accordingly the jammer uses a "detect and jam" strategy; while the transmitter-receiver
side uses the direct frequency hopping spread spectrum approach to mitigate the jamming impact. The basic
jamming/anti-jamming problem is formulated as a two-side zero sum game between the jammer and the transmitterreceiver
sides. For spectrum sensing, it is assumed that the jammer uses the energy detection in a sliding window
fashion, namely, sliding window energy detection. As a conservative strategy of the transmitter-receiver side, Maxmin
solutions to the jamming/anti-jamming game are obtained under various conditions. The impacts of factors such as
signal propagation delay, channel bandwidth, and jammer/receiver side signal noise ratio on the game results are
discussed. The results show the potential threats of cognitive jammers and provide important information for the
configuration of jamming resistant space communication networks.
The understanding of how humans process information, determine salience, and combine seemingly unrelated
information is essential to automated processing of large amounts of information that is partially relevant, or of unknown
relevance. Recent neurological science research in human perception, and in information science regarding contextbased
modeling, provides us with a theoretical basis for using a bottom-up approach for automating the management of
large amounts of information in ways directly useful for human operators. However, integration of human intelligence
into a game theoretic framework for dynamic and adaptive decision support needs a perception and cognition model. For
the purpose of cognitive modeling, we present a brain-computer-interface (BCI) based humanoid robot system to acquire
brainwaves during human mental activities of imagining a humanoid robot-walking behavior. We use the neural signals
to investigate relationships between complex humanoid robot behaviors and human mental activities for developing the
perception and cognition model. The BCI system consists of a data acquisition unit with an electroencephalograph
(EEG), a humanoid robot, and a charge couple CCD camera. An EEG electrode cup acquires brainwaves from the skin
surface on scalp. The humanoid robot has 20 degrees of freedom (DOFs); 12 DOFs located on hips, knees, and ankles
for humanoid robot walking, 6 DOFs on shoulders and arms for arms motion, and 2 DOFs for head yaw and pitch
motion. The CCD camera takes video clips of the human subject's hand postures to identify mental activities that are
correlated to the robot-walking behaviors. We use the neural signals to investigate relationships between complex
humanoid robot behaviors and human mental activities for developing the perception and cognition model.
KEYWORDS: Image segmentation, Image processing algorithms and systems, RGB color model, Data modeling, Chemical elements, Machine vision, Computer vision technology, Image processing, Medical research, Analytical research
Image segmentation decomposes a given image into segments, i.e. regions containing "similar" pixels, that aids
computer vision applications such as face, medical, and fingerprint recognition as well as scene characterization.
Effective segmentation requires domain knowledge or strategies for object designation as no universal segmentation
algorithm exists. In this paper, we propose a similarity based image segmentation approach based on game theory
methods. The essential idea behind our approach is that the similarity based clustering problem can be considered as a
"clustering game". Within this context, the notion of a cluster turns out to be equivalent to a classical equilibrium
concept from game theory, as the game equilibrium reflects both the internal and external cluster conditions. We also
show that there exists a correspondence between these equilibriums and the local solutions of a polynomial, linearlyconstrained,
optimization problem, and provide an algorithm for finding the equalibirums. Experiments on image
segmentation problems show the superiority of the proposed clustering game image segmentation (CGIS) approach
using a common data set of visual images in autonomy, speed, and efficiency.
Given the increasingly dense environment in both low-earth orbit (LEO) and geostationary orbit (GEO), a sudden
change in the trajectory of any existing resident space object (RSO) may cause potential collision damage
to space assets. With a constellation of electro-optical/infrared (EO/IR) sensor platforms and ground radar
surveillance systems, it is important to design optimal estimation algorithms for updating nonlinear object
states and allocating sensing resources to effectively avoid collisions among many RSOs. Previous work on
RSO collision avoidance often assumes that the maneuver onset time or maneuver motion of the space object
is random and the sensor management approach is designed to achieve efficient average coverage of the RSOs.
Few attempts have included the inference of an object's intent in the response to an RSO's orbital change.
We propose a game theoretic model for sensor selection and assume the worst case intentional collision of an
object's orbital change. The intentional collision results from maximal exposure of an RSO's path. The resulting
sensor management scheme achieves robust and realistic collision assessment, alerts the impending collisions,
and identifies early RSO orbital change with lethal maneuvers. We also consider information sharing among
distributed sensors for collision alert and an object's intent identification when an orbital change has been
declared. We compare our scheme with the conventional (non-game based) sensor management (SM) scheme
using a LEO-to-LEO space surveillance scenario where both the observers and the unannounced and unplanned
objects have complete information on the constellation of vulnerable assets. We demonstrate that, with adequate
information sharing, the distributed SM method can achieve the performance close to that of centralized SM in
identifying unannounced objects and making early warnings to the RSO for potential collision to ensure a proper
selection of collision avoidance action.
This paper develops and evaluates a pursuit-evasion orbital game approach for satellite interception and collision
avoidance. Using a coupled zero-sum differential pursuit-evasion game, the pursuer minimizes the satellite interception
time, and the evader tries to maximize interception time for collision avoidance. For the satellite interception problem we
design an algorithm for pursuer and one for collision avoidance, where the game solution controls the evader satellite.
The interception-avoidance (IA) game approach provides a worst-case solution, which is the robust lower-bound
performance case. We divide our IA algorithm into two parts: first, the pursuer will rotate its orbit to the same plane of
the evader; and second, the two spacecraft will play a zero-sum pursuit-evasion (PE) game. A two-step setup saves
energy during the PE game because rotating a pursuer orbit requires more energy than maneuvering within the orbit
plane. For the PE orbital game, an optimum open loop feedback saddle-point equilibrium solution is calculated between
the pursuer and evader control structures. Using the open-loop feedback control rule, each player will calculate their
distributed control track state. Numerical simulations are calculated to demonstrate the performance.
Sensor allocation is an important and challenging problem within the field of multi-agent systems. The sensor allocation
problem involves deciding how to assign a number of targets or cells to a set of agents according to some allocation
protocol. Generally, in order to make efficient allocations, we need to design mechanisms that consider both the task
performers' costs for the service and the associated probability of success (POS). In our problem, the costs are the used
sensor resource, and the POS is the target tracking performance. Usually, POS may be perceived differently by different
agents because they typically have different standards or means of evaluating the performance of their counterparts
(other sensors in the search and tracking problem). Given this, we turn to the notion of trust to capture such subjective
perceptions. In our approach, we develop a trust model to construct a novel mechanism that motivates sensor agents to
limit their greediness or selfishness. Then we model the sensor allocation optimization problem with trust-in-loop
negotiation game and solve it using a sub-game perfect equilibrium. Numerical simulations are performed to
demonstrate the trust-based sensor allocation algorithm in cooperative space situation awareness (SSA) search problems.
In the modern networked battlefield, network centric warfare (NCW) scenarios need to interoperate between shared
resources and data assets such as sensors, UAVs, satellites, ground vehicles, and command and control (C2/C4I)
systems. By linking and fusing platform routing information, sensor exploitation results, and databases (e.g. Geospatial
Information Systems [GIS]), the shared situation awareness and mission effectiveness will be improved. Within the
information fusion community, various research efforts are looking at open standard approaches to composing the
heterogeneous network components under one framework for future modeling and simulation applications. By utilizing
the open source services oriented architecture (SOA) based sensor web services, and GIS visualization services, we
propose a framework that ensures the fast prototyping of intelligence, surveillance, and reconnaissance (ISR) system
simulations to determine an asset mix for a desired mission effectiveness, performance modeling for sensor
management and prediction, and user testing of various scenarios.
This paper is concerned with the nonlinear filtering problem for tracking a space object with possibly delayed
measurements. In a distributed dynamic sensing environment, due to limited communication bandwidth and
long distances between the earth and the satellites, it is possible for sensor reports to be delayed when the
tracking filter receives them. Such delays can be complete (the full observation vector is delayed) or partial (part
of the observation vector is delayed), and with deterministic or random time lag. We propose an approximate
approach to incorporate delayed measurements without reprocessing the old measurements at the tracking filter.
We describe the optimal and suboptimal algorithms for filter update with delayed measurements in an orbital
trajectory estimation problem without clutter. Then we extend the work to a single object tracking under clutter
where probabilistic data association filter (PDAF) is used to replace the recursive linear minimum means square
error (LMMSE) filter and delayed measurements with arbitrary lags are be handled without reprocessing the
old measurements. Finally, we demonstrate the proposed algorithms in realistic space object tracking scenarios
using the NASA General Mission Analysis Tool (GMAT).
In this paper, we propose a solution to the cooperative path planning with limited communication problem in two phases.
In the first (offline) phase, a Pareto-optimal path problem is formulated to find a reference path and the graph cuts
minimization method is used to speedily calculate the optimal solution. In the second (online) phase, a foraging
algorithm is used to dynamically refine the reference path to meet the dynamic constraints of unmanned aerial vehicle
(UAVs), during which an open-loop feedback optimal (OLFO) controller is used to estimate the states which may be
unavailable due to infrequent battlefield information updates. Furthermore, an adaptive Markov decision process is
proposed to deal with intermittent asynchronous information flow. The method is demonstrated in a simulation for a
swarm of Unmanned Air Vehicle (UAV) teams with various communication ranges.
This paper develops and evaluates a game-theoretic approach to distributed sensor-network management for target
tracking via sensor-based negotiation. We present a distributed sensor-based negotiation game model for sensor
management for multi-sensor multi-target tacking situations. In our negotiation framework, each negotiation agent
represents a sensor and each sensor maximizes their utility using a game approach. The greediness of each sensor is
limited by the fact that the sensor-to-target assignment efficiency will decrease if too many sensor resources are assigned
to a same target. It is similar to the market concept in real world, such as agreements between buyers and sellers in an
auction market. Sensors are willing to switch targets so that they can obtain their highest utility and the most efficient
way of applying their resources. Our sub-game perfect equilibrium-based negotiation strategies dynamically and
distributedly assign sensors to targets. Numerical simulations are performed to demonstrate our sensor-based negotiation
approach for distributed sensor management.
KEYWORDS: Sensors, Active remote sensing, Sensor networks, Data processing, Active sensors, Data modeling, Algorithm development, Sensor performance, Fusion energy, Analytical research
In this paper, we studied the performance metrics for evaluating Network-Centric Warfare (NCW) battlefield awareness.
We developed a set of novel information awareness metrics to enable responsive situation assessment under mission-critical
conditions. The awareness metrics model (AMM) reflects the global information values of event locations such
as position, terrain information, dangerousness, survivability, cell difficulty, and mission importance. Based on the
enhanced awareness model, we developed an in-network cooperative multi-sensor search and track (ICMS) algorithm by
solving a unified optimization problem in which each cell is searched and all detected objects are tracked for at least a
desired track-lifetime period. We utilize a track-lifetime surface metric to represent the spatial and temporal aspects of
object movements over a region of interest that requires frequent sampling of the known and estimated object positions
(track maintenance) as well as possible object arrivals (track initiation). To demonstrate the effectiveness of our
approach, we implemented our ICMS algorithm in a numerical example and found that it is effective in the sense that
most cells with high activity are well-searched.
KEYWORDS: Satellites, Space operations, Data fusion, Information fusion, Solar processes, Meteorological satellites, Sensors, Kinematics, Control systems, Stochastic processes
This paper proposes a Markov (stochastic) game theoretic level-3 data fusion approach for defensive counterspace.
Based on the Markov game theory and the advanced knowledge infrastructures for information fusion, the approach can
enhance threat detection, validation, and mitigation for future counterspace and space situational awareness (SSA)
operations. A Markov game is constructed to model the possible interactions between the dynamic and intelligent threats
and friendly satellites, and effects of various space weather conditions. To systematically solve the complicated Markov
game, a conversion from general Markov games into several Markov Decision Processes (MDPs) as well as some static
bi-matrix games is provided. The proposed Markov game model and innovative solution are demonstrated in a numerical
example.
KEYWORDS: Sensors, Data fusion, Data modeling, Information fusion, Control systems, Weapons, Warfare, Sensor networks, Detection and tracking algorithms, Algorithm development
In this paper, we have proposed a highly innovative advanced command and control framework for sensor networks
used for future Integrated Fire Control (IFC). The primary goal is to enable and enhance target detection, validation, and
mitigation for future military operations by graphical game theory and advanced knowledge information fusion
infrastructures. The problem is approached by representing distributed sensor and weapon systems as generic warfare
resources which must be optimized in order to achieve the operational benefits afforded by enabling a system of systems.
This paper addresses the importance of achieving a Network Centric Warfare (NCW) foundation of information
superiority-shared, accurate, and timely situational awareness upon which advanced automated management aids for
IFC can be built. The approach uses the Data Fusion Information Group (DFIG) Fusion hierarchy of Level 0 through
Level 4 to fuse the input data into assessments for the enemy target system threats in a battlespace to which military
force is being applied. Compact graph models are employed across all levels of the fusion hierarchy to accomplish
integrative data fusion and information flow control, as well as cross-layer sensor management. The functional block at
each fusion level will have a set of innovative algorithms that not only exploit the corresponding graph model in a
computationally efficient manner, but also permit combined functional experiments across levels by virtue of the
unifying graphical model approach.
This paper proposes an innovative data-fusion/ data-mining game theoretic situation awareness and impact assessment
approach for cyber network defense. Alerts generated by Intrusion Detection Sensors (IDSs) or Intrusion Prevention
Sensors (IPSs) are fed into the data refinement (Level 0) and object assessment (L1) data fusion components. High-level
situation/threat assessment (L2/L3) data fusion based on Markov game model and Hierarchical Entity Aggregation
(HEA) are proposed to refine the primitive prediction generated by adaptive feature/pattern recognition and capture new
unknown features. A Markov (Stochastic) game method is used to estimate the belief of each possible cyber attack
pattern. Game theory captures the nature of cyber conflicts: determination of the attacking-force strategies is tightly
coupled to determination of the defense-force strategies and vice versa. Also, Markov game theory deals with uncertainty
and incompleteness of available information. A software tool is developed to demonstrate the performance of the high
level information fusion for cyber network defense situation and a simulation example shows the enhanced understating
of cyber-network defense.
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