This PDF file contains the front matter associated with SPIE-IS&T Proceedings Volume 7480, including the Title Page, Copyright information, Table of Contents, and Conference Committee listing.

The United States Department of Defense (DoD) defines 'force protection' as "preventive measures taken to mitigate hostile actions against DoD personnel (to include family members), resources, facilities, and critical information." Advanced technologies enable significant improvements in automating and distributing situation awareness, optimizing operator time, and improving sustainability, which enhance protection and lower costs. The JFPASS Joint Capability Technology Demonstration (JCTD) demonstrates a force protection environment that combines physical security and Chemical, Biological, Radiological, Nuclear, and Explosive (CBRNE) defense through the application of integrated command and control and data fusion. The JFPASS JCTD provides a layered approach to force protection by integrating traditional sensors used in physical security, such as video cameras, battlefield surveillance radars, unmanned and unattended ground sensors. The optimization of human participation and automation of processes is achieved by employment of unmanned ground vehicles, along with remotely operated lethal and less-than-lethal weapon systems. These capabilities are integrated via a tailorable, user-defined common operational picture display through a data fusion engine operating in the background. The combined systems automate the screening of alarms, manage the information displays, and provide assessment and response measures. The data fusion engine links disparate sensors and systems, and applies tailored logic to focus the assessment of events. It enables timely responses by providing the user with automated and semi-automated decision support tools. The JFPASS JCTD uses standard communication/data exchange protocols, which allow the system to incorporate future sensor technologies or communication networks, while maintaining the ability to communicate with legacy or existing systems.

This paper describes an innovative miniature optical sensor for predicting dissolved oxygen concentrations and measuring turbulence in river and littoral water columns. The dissolved oxygen and turbulence sensor consists of a single-frequency laser transmitter and a photodetector on which the scattered light from the turbulent water at the base of a dam or spillway is coherently mixed with a sample of the transmitted beam. This miniature sensor could be used both upstream and downsteam of dams and weirs to predict the amount of dissolved oxygen and turbulence in these waters. It could also be used on mobile platforms, such as unmanned underwater vehicles (UUV's), to monitor the edges of biological or chemical plumes or for wake follow platforms, schools of fish or marine mammals or on stationary unattended underwater sensors to monitor natural aeration and turbulence in littoral and riverine waters. Arrays of fixed unattended sensors could be used to detect the wake of transiting submerged vehicles, scuba divers, marine mammals or large schools of fish. A mobile platform equipped with a miniature sensor could to be cued to the general location and depth of an underwater target and then the platform could use this small aperture sensor to acquire and follow the wake. This dissolved oxygen and turbulence sensor system could be miniaturized and packaged into a very small volume; approximately the size of a wristwatch.

With over 360 ports of entry and 20 million sea, truck, and rail containers entering the United States every year, port facilities pose a large risk to security. Securing these ports and monitoring the variety of traffic that enter and leave is a major task. To accomplish this, the authors propose a fully distributed building block approach to port security. Based on prior work accomplished in the design and fielding of an intelligent transportation system in the United States, building blocks can be assembled, mixed and matched, and scaled to provide a comprehensive security system. Network blocks, surveillance blocks, sensor blocks, and display blocks will be developed and demonstrated in the lab, and at an inland port. The following functions will be demonstrated and scaled through analysis and demonstration: Barge tracking, credential checking, container inventory, vehicle tracking, and situational awareness. The concept behind this research is "any operator on any console can control any device at any time."

Surveillance is an important application of sensor networks. In this paper it is demonstrated how a sparse network of stationary infrared (IR) sensors with highly directional, stationary beam patterns based on the LIDAR principle can be used to reliably track persons. Due to the small number of sensors and their narrow beam patterns a significant portion of the area to be surveilled is not directly assessed by the sensors. To nonetheless achieve reliable tracking of moving targets in the entire area to be monitored, we employ the most appropriate sensor network configuration and propose a probabilistic tracking approach. The behavior of a person moving through the area of observation is classified as "normal" or "abnormal" depending upon the trajectory and motion dynamics. The classification is based on a linear Kalman prediction.

A distributed technology will be presented enabling a remote operator to manage arbitrary sized groups of stationary or mobile sensors (or robots), behaving altogether as an integral and global-goal-driven unit. The group is tasked in a Distributed Scenario Language (DSL) collectively executed by communicating interpreters embedded in individual sensors and integrated with their functionalities. Compact and created on the fly, DSL scenarios can be remotely injected into any sensor, subsequently self-replicating, self-modifying, and self-spreading in a virus mode throughout the whole group, tasking individual units and setting needed operational infrastructures among them. The approach can remotely control dynamic and open systems of different natures and comprehend complex phenomena unavailable to individual sensors.

Some interest has recently been shown in determining the reliability of unmanned ground vehicles network. Based on the predicted reliability, a commander can take appropriate action in the battle field operation for unmanned ground vehicles network. The reliability should include coordination and collaboration of a number of different unmanned ground vehicles. Some approaches for determining the reliability of unmanned vehicles have been discussed in the literatures. In this paper, we propose a new algorithm by which the reliability of unmanned ground vehicles network is predicted using fuzzy approach. The approach is different from statistical approaches discussed in previous papers. The algorithm suggested here is based on all simple paths obtained between the terminal nodes of the network in question or the cutset expression of the terminal nodes. Each variable in the Boolean expression obtained from simple path method or from the cutset method will be subjectively assigned a membership grade by an expert in the field. Fuzzy union and fuzzy intersection operations are used to predict the reliability of the network in question. New theorems have been developed to determine the disjoint expression using fuzzy logic. The comparison of this new approach is given with the existing approaches. Further, assuming node reliability and branch reliability one can also predict system reliability of the unmanned ground vehicles network using fuzzy logic.

There is an increasing interest in the army of small unmanned robots taking part in defense operations. It is considered important to predict the reliability of the group of robots taking part in different operations. A group of robots have both coordination and collaboration. The robot operations are considered as a network graph whose system reliability can be determined with the help of different techniques. Once a specified reliability is achieved the commander controlling the operation can take appropriate action. This paper gives a simulation which can determine the system reliability of the robotic systems having collaboration and coordination. The procedure developed is based on binary decision diagrams to obtain a disjoint Boolean expression. The procedure is applicable for any number of nodes and the branches. For illustration purposes reliability of simple circuits like series network, parallel network, series-parallel and non-series parallel network are illustrated. It is hoped that more work in this area will lead to the development of algorithms which will be ultimately used for a real time environment.

The use of miniature Unmanned Aerial Vehicles (UAVs), e.g. quadrocopters, has gained great popularity over the last years. Some complex application scenarios for micro UAVs call for the formation of swarms of multiple drones. In this paper a platform for the creation of such swarms is presented. It consists of commercial quadrocopters enhanced with on-board processing and communication units enabling autonomy of individual drones. Furthermore, a generic ground control station has been realized. Different co-operation strategies for teams of UAVs are currently evaluated with an agent based simulation tool. Finally, complex application scenarios for multiple micro UAVs are presented.

Mobile Optical Free-Space Communication (MFSO) downlinks from observation platforms in low earth orbit or in the atmosphere will allow nearly undetectable high-speed data links with small and low-power laser communication terminals. Several research institutions and companies are developing MFSO terminal technology for application in tactical and strategic communication scenarios as well as for civilian and security purposes. DLR is advancing this technology based on mature terrestrial fiber link components towards reliable and cost-effective free-space links. Successful demonstrations in the lower troposphere, the stratosphere, and from space have shown the potential of this technology. Future applications of MFSO will cover high-speed space downlinks, frequency distribution for navigation purposes, secure quantum key distribution, bidirectional links between aeronautic nodes, as well as near ground links between vehicles or ships. In this paper we will present aspects of the system design, calculate link performance limits, and show results of aeronautic and space downlink trials.

This paper describes recent progress in developing a wireless optical link between the fuselage of a cockpit and an aviation helmet. Such a link is desired to replace the physical umbilical cable existing in current cockpit systems, for reasons of potential bandwidth, immunity to EM interference, and freedom from physical constraints within the cockpit. The link concept consists of multiple transmitters embedded in the cockpit fuselage, each sending video (or symbology) data out in a cone of light over free space, which is detected by an array of receivers positioned on the helmet - the data is then sent to the eyepieces or visor of the pilot (after any intermediate processing). The design is such that one of these links is always maintained throughout possible movement of the head. In a recent proof-of-principle demonstration we showed uncompressed, 100 Mbps video data streamed live from the fuselage of a cockpit simulator to an angled cluster of silicon-based receivers mounted on the helmet, via a pair of ~1 Watt free-space lasers operating at 810 nm. Fast Ethernet media converters were used here for convenience and cost. The bespoke optical and electrical link components were developed in close collaboration with suppliers. The system performance arises from: the high dynamic range of the receivers (up to 25 dB), which are equipped with optical antennae to magnify the optical gain; the high power of the lasers; and the switching electronics used to control the signal path on the helmet. Future potential improvements to the technology are discussed, with an indication of wireless link requirements for relevant BAE Systems applications.

The 1550nm wavelength region is critical to the development of next generation eye safe military applications such as range finding and friend or foe identification (FOE). So far the relatively low laser external efficiency was a strong limiting factor favoring shorter wavelength diode lasers. We report on the development of a new monolithic multiple junction pulsed laser diode offering an external efficiency of more than one Watt per Amp with high brightness. Peak optical output power of more than 37 Watts has been achieved from a single multi-junction diode laser. Divergence is narrow with less than 35 degrees (FWHM) in the fast axis direction. Starting from an AlGaInAs quantum well laser structure, we show the criticality of the design of InP based tunnel junctions to the growth of the three layer epitaxial monolithic laser. We then report on trenches employed to confine carriers under the contacting stripe and on growth strategies used to decouple the multiple light sources resulting from the multi-junction design. A full set of characterization data is presented concluding with a discussion on performance limitations and their potential causes.

We report on a laser communications experiment over a kilometre optical range where we have used a retro-reflective transponder incorporating an optical modulator based on silicon micro-electro-mechanical systems (MEMS) device. This employs interference to provide modulation and relies on performing as a coherent array to modulate incident light in the near-IR band (1550nm) over a wide angular range (120 degrees). Modulation is achieved by tuning a large array of Fabry-Perot cavities via the application of an electrostatic force to adjust the gap between a moveable mirror and the underlying silicon substrate. The micro-mirrors have a strong mechanical resonance, and modulate light by adjusting the spacing between the micromirrors and the substrate. We use a 'release and catch' technique to exploit the mechanical resonance, and we time the motion of the micro-mirrors to be synchronised with the arrival of an interrogator pulse to ensure that the etalon spacing provides the required modulation, whatever the angle of incidence. We describe experiments over a one kilometre path where simple strings were sent at 200kbit per second. We also discuss approaches to adapting the link to a given angle of incidence.

This paper proposes an approach to integrate the self-organizing map (SOM) and kernel density estimation (KDE) techniques for the anomaly-based network intrusion detection (ABNID) system to monitor the network traffic and capture potential abnormal behaviors. With the continuous development of network technology, information security has become a major concern for the cyber system research. In the modern net-centric and tactical warfare networks, the situation is more critical to provide real-time protection for the availability, confidentiality, and integrity of the networked information. To this end, in this work we propose to explore the learning capabilities of SOM, and integrate it with KDE for the network intrusion detection. KDE is used to estimate the distributions of the observed random variables that describe the network system and determine whether the network traffic is normal or abnormal. Meanwhile, the learning and clustering capabilities of SOM are employed to obtain well-defined data clusters to reduce the computational cost of the KDE. The principle of learning in SOM is to self-organize the network of neurons to seek similar properties for certain input patterns. Therefore, SOM can form an approximation of the distribution of input space in a compact fashion, reduce the number of terms in a kernel density estimator, and thus improve the efficiency for the intrusion detection. We test the proposed algorithm over the real-world data sets obtained from the Integrated Network Based Ohio University's Network Detective Service (INBOUNDS) system to show the effectiveness and efficiency of this method.

The project aims at obtaining high integrity and goal orientation of distributed dynamic systems, which may include multiple wireless sensors and mobile robots, as well as humans. The technology developed is based on the ideology of gestalt, where the whole is considered first, dominating over parts and dynamically defining their role and even existence in the context of changing goals and states of environment. Spatial mission scenarios, which may be created on the fly, are represented in a compact non-agents form collectively executed by the intelligent network of interpreters embedded into sensitive points of the system to be managed. The approach allows us to provide effective asymmetric runtime solutions to complex asymmetric problems and fulfill objectives in unpredictable environments, paving the way to massive robotization of advanced civil and military systems. The paper covers a broad spectrum of topics from philosophy and ideology to system management, to novel distributed control technology and its implementation, and to a variety of important applications. The paradigm described may be considered as the first implementation of the idea of gestalt to management of open distributed systems, as well as the first globally programmable universal super-machine dynamically covering distributed worlds and operating with both information and matter without any central resources.

In current military operations threats should be monitored accurately. The use of sensors is indispensable for this purpose, for example with camera and radar systems. Using data from such systems we have studied automated procedures for extracting observable behavioral features of persons and groups, which can be associated with threats. We have analysed algorithms for identifying animals versus humans, and for determining the activity of detected humans. Secondly, geospatial algorithms are studied to determine people in suspicious places.

Detection and tracking of a varying number of people is very essential in surveillance sensor systems. In the real applications, due to various human appearance and confusors, as well as various environmental conditions, multiple targets detection and tracking become even more challenging. In this paper, we proposed a new framework integrating a Multiple-Stage Histogram of Oriented Gradients (HOG) based human detector and the Particle Filter Gaussian Process Dynamical Model (PFGPDM) for multiple targets detection and tracking. The Multiple-Stage HOG human detector takes advantage from both the HOG feature set and the human motion cues. The detector enables the framework detecting new targets entering the scene as well as providing potential hypotheses for particle sampling in the PFGPDM. After processing the detection results, the motion of each new target is calculated and projected to the low dimensional latent space of the GPDM to find the most similar trained motion trajectory. In addition, the particle propagation of existing targets integrates both the motion trajectory prediction in the latent space of GPDM and the hypotheses detected by the HOG human detector. Experimental tests are conducted on the IDIAP data set. The test results demonstrate that the proposed approach can robustly detect and track a varying number of targets with reasonable run-time overhead and performance.

This paper presents a high efficiency algorithm, Multiple Analytical Distribution Filter (MADF), to estimate location for underwater navigation. Using small grid sampling around candidate areas of high probability, MADF computes probabilities directly from the known analytical distributions of each beacon. The algorithm is deterministic and achieves similar results to particle filters, but at a lower computational cost in our tests. MADF and particle filters represent improvements over Kalman Filters for environments characterized by non-Gaussian noise distribution.

Proposed here is a series of techniques exploiting micro-Doppler ultrasonic sensors capable of characterizing various detected mammalian targets based on their physiological movements captured a series of robust features. Employed is a combination of unique and conventional digital signal processing techniques arranged in such a manner they become capable of classifying a series of walkers. These processes for feature extraction develops a robust feature space capable of providing discrimination of various movements generated from bipeds and quadrupeds and further subdivided into large or small. These movements can be exploited to provide specific information of a given signature dividing it in a series of subset signatures exploiting wavelets to generate start/stop times. After viewing a series spectrograms of the signature we are able to see distinct differences and utilizing kurtosis, we generate an envelope detector capable of isolating each of the corresponding step cycles generated during a walk. The walk cycle is defined as one complete sequence of walking/running from the foot pushing off the ground and concluding when returning to the ground. This time information segments the events that are readily seen in the spectrogram but obstructed in the temporal domain into individual walk sequences. This walking sequence is then subsequently translated into a three dimensional waterfall plot defining the expected energy value associated with the motion at particular instance of time and frequency. The value is capable of being repeatable for each particular class and employable to discriminate the events. Highly reliable classification is realized exploiting a classifier trained on a candidate sample space derived from the associated gyrations created by motion from actors of interest. The classifier developed herein provides a capability to classify events as an adult humans, children humans, horses, and dogs at potentially high rates based on the tested sample space. The algorithm developed and described will provide utility to an underused sensor modality for human intrusion detection because of the current high-rate of generated false alarms. The active ultrasonic sensor coupled in a multi-modal sensor suite with binary, less descriptive sensors like seismic devices realizing a greater accuracy rate for detection of persons of interest for homeland purposes.

A group of acoustic arrays that provide direction of approach estimates also support classification of vehicles using the beams formed during that estimation. Successful simultaneous tracking and classification has demonstrated the value of such a sensing resource as a UGS installation. We now consider potential attacks on the integrity of such an installation, describing the effect of compromised acoustic arrays in the data analysis and tracking and classification results. We indicate how these can be automatically recognized, and note that calibration methods intended for deployment time can be used for recovery during operation, which opens the door to methods for recovery from the compromise without re-configuring the equipment, using abductive reasoning to discover the necessary re-processing structure. By rotating an acoustic array, the tracking stability and implied path of a tracked entity can be distorted while leaving the data and analysis from individual arrays self-consistent. Less structured modifications, such as unstructured re-ordering of microphone connections, impact the basic data analysis. We examine the effect of these classes of attack on the integrity of a set of unattended acoustic arrays, and consider the steps necessary for detection, diagnosis, and recovering an effective sensing system. Understaning these steps plays an important part in reasoning in support of balance of investment, planning, operation and post-hoc analysis.

A Mid-Wave Infra-Red (MWIR) detector is developed by doping an n-type 4H-SiC with an appropriate dopant to create a dopant energy level that matches with a quantum of energy for the wavelength of interest. The detector absorbs the photons and the absorbed photon energy modifies the electron density in the semiconductor by the photoexcitation, leading to changes in the refraction index. Ga is known to have an energy level of 0.30 eV in n-type 4H-SiC substrates, which corresponds to the wavelength 4.21 μm. A detector was fabricated for the MWIR wavelength of 4.21 μm by doping n-type 4H-SiC with Ga. The dopant energy level was confirmed by optical absorption measurements in the wavelength range of 4 to 5 μm. The optical response of the detector to the wavelength 4.21 μm was determined by measuring the reflectivity of the detector using a He-Ne laser of wavelength 632.8 nm as the probe beam. The reflectivity data were used to calculate the variation in the refraction index of the detector at the MWIR wavelength of interest and the selectivity of the detector was established by testing the sensor response to that of an as-received sample. The comparison yielded a distinct change in the refraction index curve for the detector, indicating that the detector is suitable for applications at the wavelength 4.21 μm.

A method for precise geo-location of objects that are observed by an airborne camera is described in this paper. The platform for image acquisition is a micro aerial vehicle (MAV) with an integrated navigation system. From the captured image sequences and MAV navigation data, the three-dimensional positions of objects of interest are retrieved. Different techniques for image feature tracking are compared. Combining measurements from multiple viewpoints in a Bundle Adjustment process yields optimal accuracy of the estimated object positions. The robustness of the optimization is enhanced by tight integration of data from both the vision and the navigation system.

In this paper, the development of a vision based system for a small-scale VTOL-MAV is presented. The on-board GPS/INS navigation system is augmented by further sensors in order to allow for an autonomous waypoint mode. Especially in urban environments the GPSsignal quality is disturbed by shading and multipath propagation. The investigated vision system based on algorithms analyzing the optical flow is essential to enable the helicopter to reliably hover even in these scenarios. Due to the integration of the vision based navigation information into the navigation filter, GPSsignal outages can be bridged. The necessary height above ground information is estimated from the relative altitude change given by the barometric altimeter and the optical flow.

Sepsis causes 9.3% of overall deaths in United States. To diagnose sepsis, cell/bacteria capture and culturing methods have been widely investigated in the medical field. Escherichia Coli (E. Coli) is used as a model organism for sepsis in blood stream since wide variety of antibodies are established and the genetic modification process is well documented for fluorescent tagging. In point-of-care testing applications, the sepsis diagnostics require fast monitoring, inexpensive testing, and reliable results at resource limited settings, i.e. battle field, home care for dialysis. However, the cell/E.coli are hard to directly capture and see at the POCT because of the small size, 2 μm long and 0.5 μm in diameter, and the bacteria are rare in the blood stream in sepsis. Here, we propose a novel POCT platform to image and enumerate cell/E.coli on a microfluidic surface to diagnose sepsis at resource limited conditions. We demonstrate that target cells are captured from 5 μl of whole blood using specific antibodies and E.coli are imaged using a lens-free imaging platform, 2.2 μm pixel CMOS based imaging sensor. This POCT cell/bacteria capture and enumeration approach can further be used for medical diagnostics of sepsis. We also show approaches to rapidly quantify white blood cell counts from blood which can be used to monitor immune response.

In recent years the use of Unmanned Autonomous Vehicles (UAV) has seen a wider range of applications. However, their applications are restricted due to (a) advanced integrated sensing and processing electronics and (b) limited energy storage or on-board energy generation to name a few. The availability of a wide variety of sensing elements, operating at room temperatures, provides a great degree of flexibility with an extended application domain. Though sensors responding to a variable spectrum of input excitations ranging from (a) chemical, (b) biological, (c) atmospheric, (d) magnetic and (e) visual/IR imaging have been implemented in UAVs, the use of THz as a technology has not been implemented due to the absence of systems operating at room temperature. The integration of multi-phenomenological onboard sensors on small and miniature unmanned air vehicles will dramatically impact the detection and processing of challenging targets, such as humans carrying weapons or wearing suicide bomb vests. Unmanned air vehicles have the potential of flying over crowds of people and quickly discriminating non-threat humans from treat humans. The state of the art in small and miniature UAV's has progressed to vehicles of less than 1 pound in weight but with payloads of only a fraction of a pound. Uncooled IR sensors, such as amorphous silicon and vanadium oxide microbolometers with MRT's of less than 70mK and requiring power of less than 250mW, are available for integration into small UAV's. These sensors are responsive only up to approximately 14 microns and do not favorably compare with THz imaging systems for remotely detecting and classifying concealed weapons and bombs. In the following we propose the use of THz GaN-based QCL operating at room temperature as a possible alternative.