This PDF file contains the front matter associated with SPIE Proceedings Volume 9825, including the Title Page, Copyright information, Table of Contents, Introduction (if any), and Conference Committee listing.

The Department of Defense (DoD) established an Executive Agent for the DoD’s Non-Lethal Weapons Program in 1996. DoD Directive 3000.03E, DoD Executive Agent for Non-Lethal Weapons (NLW), and NLW Policy¹ defines policies and responsibilities for the development and employment of non-lethal weapons. It also designates the Commandant of the Marine Corps as the DoD NLW Executive Agent with the responsibility to serve as the DoD focal point on all matters. An important component of this responsibility is development of the DoD’s NLW technology development strategy and investment in promising technologies that will enable advanced nonlethal capabilities to support the warfighter in future operating environments.

There are two acoustical events associated with small arms fire: the muzzle blast (created by bullets being expelled from the barrel of the weapon), and the shockwave (created by bullets which exceed the speed of sound). Assuming the ballistics of a round are known, the times and directions of arrival of the acoustic events furnish sufficient information to determine the origin of the shot. Existing methods tacitly assume that it is a single sensor which makes measurements of the times and direction of arrival. If the sensor is located past the point where the bullet goes transonic or if the sensor is far off the axis of the shot line a single sensor localization become highly inaccurate due to the ill-conditioning of the localization problem. In this paper, a more general approach is taken which allows for localizations from measurements made at separate locations. There are considerable advantages to this approach, the most noteworthy of which is the improvement in localization accuracy due to the improvement in the conditioning of the problem. Additional benefits include: the potential to locate in cases where a single sensor has insufficient information, furnishing high quality initialization to data fusion algorithms, and the potential to identify the round from a set of possible rounds.

The state of art in shock resistant MEMS accelerometer design is to reduce the size of the proof-mass, thereby reducing the generated forces and moments due to shock loading. Physical stops are also provided to limit proof-mass motion to prevent damage to various moving components. The reduction of the proof-mass size reduces the sensor sensitivity. In addition, to increase the sensor dynamic response, proof-mass motion needs to be minimally damped, resulting in a significant sensor settling time after experiencing a high shock loading such as those experienced by gun-fired munitions during firing. The settling time is particularly important for accelerometers that are used in gun-fired munitions and mortars for navigation and guidance. This paper describes the development of a novel class of accelerometers that are provided with the means of locking the sensor proof-mass in its “null” position when subjected to acceleration levels above a prescribed threshold, thereby protecting the moving parts of the accelerometer. In munitions applications, the proof-mass is thereby locked in its null position during the firing and is released during the flight to measure flight acceleration with minimal settling time. Details of the design and operation of the developed sensors and results of their prototyping and testing are presented. The application of the developed technology to other types of inertial sensors and devices is discussed.

Proposed airborne surveillance radar system can detect, locate, track, and classify low-profile, low-altitude targets: from traditional fixed and rotary wing aircraft to non-traditional targets like unmanned aircraft systems (drones) and even small projectiles. Distributed micro-radar system is the next step in the development of passive monopulse direction finder proposed by Stephen E. Lipsky in the 80s. To extend high frequency limit and provide high sensitivity over the broadband of frequencies, multiple angularly spaced directional antennas are coupled with front end circuits and separately connected to a direction finder processor by a digital interface. Integration of antennas with front end circuits allows to exclude waveguide lines which limits system bandwidth and creates frequency dependent phase errors. Digitizing of received signals proximate to antennas allows loose distribution of antennas and dramatically decrease phase errors connected with waveguides. Accuracy of direction finding in proposed micro-radar in this case will be determined by time accuracy of digital processor and sampling frequency. Multi-band, multi-functional antennas can be distributed around the perimeter of a Unmanned Aircraft System (UAS) and connected to the processor by digital interface or can be distributed between swarm/formation of mini/micro UAS and connected wirelessly. Expendable micro-radars can be distributed by perimeter of defense object and create multi-static radar network. Low-profile, lowaltitude, high speed targets, like small projectiles, create a Doppler shift in a narrow frequency band. This signal can be effectively filtrated and detected with high probability. Proposed micro-radar can work in passive, monostatic or bistatic regime.

This paper presents the findings of using convolutional neural networks (CNNs) to classify human activity from micro-Doppler features. An emphasis on activities involving potential security threats such as holding a gun are explored. An automotive 24 GHz radar on chip was used to collect the data and a CNN (normally applied to image classification) was trained on the resulting spectrograms. The CNN achieves an error rate of 1.65 % on classifying running vs. walking, 17.3 % error on armed walking vs. unarmed walking, and 22 % on classifying six different actions.

In this paper we present a discussion of using an array of atomic magnetometers to locate the presence of ferrous materials, such as concealed weapons, in real time. Ferrous materials create magnetic field anomalies. In order to determine the location of such objects, readings from many positions must be analyzed. This field inversion is typically done in post processing, once readings over a survey area or region of interest have been gathered. With the recent development of small and low power sensors, the dozen or so sensors required to provide information for magnetic field inversion may be deployed. We have built such an array and present here the results of using a realtime inversion algorithm. The inversion algorithm accurately determines target properties at a rate of 10 times per second as objects move past the array. Accuracies are as good as those obtained with target inversion methods used in analyzing data for unexploded ordnance detection. While those methods are typically applied in post processing, we show here those methods work even better when applied in real-time. We further present some analyses of the predicted performance of arrays in various geometries to address issues in security, such as crowd or perimeter monitoring. Target inversion methods may be accurately simulated, allowing for the development and testing of algorithms in an efficient manner. Additional processing may be done using the time history of the inversion results to remove false alarms and enhance detection. The key step is to start with an inversion method, utilizing the mathematical properties of magnetic fields and the known geometry of the measurements.

This paper considers the utility of space-based 3D printing for homeland security applications, with needs ranging from the collection of data to facilitate the detection of occurrences ranging from prospective acts of terrorism, to invasion, to natural disasters. This paper presents and evaluates multiple prospective homeland security applications for an in-space 3D printing technology. The technology’s efficacy for the fabrication, refurbishment and repair of orbital craft on-demand as well as its utility as part of a mothership for a sensor net constellation are considered.

We have developed a fingerprint software package that can be used in a wide range of applications from law enforcement to public and private security systems, and to personal devices such as laptops, vehicles, and door- locks. The software and processing units are a unique implementation of new and sophisticated algorithms that compete with the current best systems in the world. Development of the software package has been in line with the third generation of our ultrasonic fingerprinting machine¹. Solid and robust performance is achieved in the presence of misplaced and low quality fingerprints.

In this paper, Single-Target-Oriented (STO) Binary Sensor Sets (BSSs) are introduced and analyzed for C3I applications. These STO BSSs are diversified multisensors (combining IR camera, LIDAR, radar, etc.) standardized into the Binary Sensor format. By increasing the k-number of Binary Sensors within the STO paradigm, we can increase target detection predictability, thus, increasing Bayesian inference strength.

In this paper, we expand our previously proposed critical infrastructure (CI) model with time dependent stochastic elements. In the model, CI is presented as a directed graph where each vertex represents a discrete system and directed edges dependency relations between the systems. Each node is associated with a finite state machine (FSM) which represents the operational status of the system in question. In this paper we associate a probability distribution to each FSM, which accounts for the flow of time and previous confirmed sensor reading. As time passes, the uncertainty about the state of the system increases. By relying on statistical probabilities that have been previously observed or known, it is possible to make predictions about the current state of CI. We present a dependency graph modelling a subset of Finnish electric grid and mobile networks. CI components are modelled using FSM structure augmented by probabilistic elements for entropy-based calculations. The proposed model provides an estimate about the state of the critical infrastructure when only limited information is available, while taking into account the increasing uncertainty created by the passage of time.

In this paper, Bayesian Binary Sensing (BBS) is discussed as an effective tool for Bayesian Inference (BI) evaluation in interdisciplinary areas such as ISR (and, C3I), Homeland Security, QC, medicine, defense, and many others. In particular, Hilbertian Sine (HS) as an absolute measure of BI, is introduced, while avoiding relativity of decision threshold identification, as in the case of traditional measures of BI, related to false positives and false negatives.

When a spatiotemporal events happens, multi-source intelligence data is gathered to understand the problem, and strategies for solving the problem are investigated. The difficulties arising from handling spatial and temporal intelligence data represent the main problem. The map might be the bridge to visualize the data and to get the most understand model for all stakeholders. For the analysis of geodata based intelligence data, a software was developed as a working environment that combines geodata with optimized ergonomics. The interaction with the common operational picture (COP) is so essentially facilitated. The composition of the COP is based on geodata services, which are normalized by international standards of the Open Geospatial Consortium (OGC). The basic geodata are combined with intelligence data from images (IMINT) and humans (HUMINT), stored in a NATO Coalition Shared Data Server (CSD). These intelligence data can be combined with further information sources, i.e., live sensors. As a result a COP is generated and an interaction suitable for the specific workspace is added. This allows the users to work interactively with the COP, i.e., searching with an on board CSD client for suitable intelligence data and integrate them into the COP. Furthermore, users can enrich the scenario with findings out of the data of interactive live sensors and add data from other sources. This allows intelligence services to contribute effectively to the process by what military and disaster management are organized.

In this paper, we investigated the publicly available iris recognition datasets and their data capture procedures in order to determine if they are suitable for the stand-off iris recognition research. Majority of the iris recognition datasets include only frontal iris images. Even if a few datasets include off-angle iris images, the frontal and off-angle iris images are not captured at the same time. The comparison of the frontal and off-angle iris images shows not only differences in the gaze angle but also change in pupil dilation and accommodation as well. In order to isolate the effect of the gaze angle from other challenging issues including dilation and accommodation, the frontal and off-angle iris images are supposed to be captured at the same time by using two different cameras. Therefore, we developed an iris image acquisition platform by using two cameras in this work where one camera captures frontal iris image and the other one captures iris images from off-angle. Based on the comparison of Hamming distance between frontal and off-angle iris images captured with the two-camera- setup and one-camera-setup, we observed that Hamming distance in two-camera-setup is less than one-camera-setup ranging from 0.05 to 0.001. These results show that in order to have accurate results in the off-angle iris recognition research, two-camera-setup is necessary in order to distinguish the challenging issues from each other.

Acoustic-electric channels can be used to send data through metallic barriers, enabling communications where electromagnetic signals are ineffective. This paper considers an acoustic-electric channel that is formed by mounting piezoelectric transducers on metallic barriers that are separated by a thin water layer. The transducers are coupled to the barriers using epoxy and the barriers are positioned to axially-align the PZTs, maximizing energy transfer efficiency. The electrical signals are converted by the transmitting transducers into acoustic waves, which propagate through the elastic walls and water medium to the receiving transducers. The reverberation of the acoustic signals in these channels can produce multipath distortion with a significant delay spread that introduces inter-symbol interference (ISI) into the received signal. While the multipath effects can be severe, the channel does not change rapidly which makes equalization easier. Here we implement a 16-QAM system on this channel, including a method for obtaining accurate carrier frequency offset (CFO) estimates in the presence of the quasi-static multipath propagation. A raised-power approach is considered but found to suffer from excessive data noise resulting from the ISI. An alternative approach that utilizes a pilot tone burst at the start of a data packet is used for CFO estimation and found to be effective. The autocorrelation method is used to estimate the frequency of the received burst. A real-time prototype of the 16 QAM system that uses a Texas Instruments MSP430 microcontroller-based transmitter and a personal computer-based receiver is presented along with performance results.

To achieve knowledge superiority in today’s operations interoperability is the key. Budget restrictions as well as the complexity and multiplicity of threats combined with the fact that not single nations but whole areas are subject to attacks force nations to collaborate and share information as appropriate. Multiple data and information sources produce different kinds of data, real time and non-real time, in different formats that are disseminated to the respective command and control level for further distribution. The data is most of the time highly sensitive and restricted in terms of sharing. The question is how to make this data available to the right people at the right time with the right granularity. The Coalition Shared Data concept aims to provide a solution to these questions. It has been developed within several multinational projects and evolved over time. A continuous improvement process was established and resulted in the adaptation of the architecture as well as the technical solution and the processes it supports. Coming from the idea of making use of existing standards and basing the concept on sharing of data through standardized interfaces and formats and enabling metadata based query the concept merged with a more sophisticated service based approach. The paper addresses concepts for information sharing to facilitate interoperability between heterogeneous distributed systems. It introduces the methods that were used and the challenges that had to be overcome. Furthermore, the paper gives a perspective how the concept could be used in the future and what measures have to be taken to successfully bring it into operations.

Cyber resliency has become an increasingly attractive research and operational concept in cyber security. While several metrics have been proposed for quantifying cyber resiliency, a considerable gap remains between those metrics and operationally measurable and meaningful concepts that can be empirically determined in a scientific manner. This paper describes a concrete notion of cyber resiliency that can be tailored to meet specific needs of organizations that seek to introduce resiliency into their assessment of their cyber security posture.

A system of computers, networks and software has some level of vulnerability exposure that puts it at risk to criminal hackers. Presently, most vulnerability research uses data from software vendors, and the National Vulnerability Database (NVD). We propose an alternative path forward through grounding our analysis in data from the operational information security community, i.e. vulnerability data from "the wild". In this paper, we propose a vulnerability data parsing algorithm and an in-depth univariate and multivariate analysis of the vulnerability arrival and deletion process (also referred to as the vulnerability birth-death process). We find that vulnerability arrivals are best characterized by the log-normal distribution and vulnerability deletions are best characterized by the exponential distribution. These distributions can serve as prior probabilities for future Bayesian analysis. We also find that over 22% of the deleted vulnerability data have a rate of zero, and that the arrival vulnerability data is always greater than zero. Finally, we quantify and visualize the dependencies between vulnerability arrivals and deletions through a bivariate scatterplot and statistical observations.

The hidden Markov model (HMM) is widely used to model time series data. However, the conventional Baum- Welch algorithm is known to perform poorly when applied to long observation sequences. The literature contains several alternatives that seek to improve the memory or time complexity of the algorithm. However, for an HMM with N states and an observation sequence of length T, these alternatives require at best O(N) space and O(N²T) time. Given the preponderance of applications that increasingly deal with massive amounts of data, an alternative whose time is O(T)+poly(N) is desired. Recent research presents an alternative to the Baum-Welch algorithm that relies on nonnegative matrix factorization. This document examines the space complexity of this alternative approach and proposes further optimizations using approaches adopted from the matrix sketching literature. The result is a streaming algorithm whose space complexity is constant and time complexity is linear with respect to the size of the observation sequence. The paper also presents a batch algorithm that allow for even further improved space complexity at the expense of an additional pass over the observation sequence.

The present article discusses some inroads in nonlinear signal processing made by the prime algorithm developer, Dr. Albert H. Nuttall and co-authors, a consortium of research scientists from the Naval Undersea Warfare Center Division, Newport, RI. The algorithm, called the Nuttall-Wiener-Volterra 'NWV' algorithm is named for its principal contributors ^{[1], [2],[ 3]} over many years of developmental research. The NWV algorithm significantly reduces the computational workload for characterizing nonlinear systems with memory. Following this formulation, two measurement waveforms on the system are required in order to characterize a specified nonlinear system under consideration: (1) an excitation input waveform, x(t) (the transmitted signal); and, (2) a response output waveform, z(t) (the received signal). Given these two measurement waveforms for a given propagation channel, a 'kernel' or 'channel response', h= [h₀,h₁,h₂,h₃] between the two measurement points, is computed via a least squares approach that optimizes modeled kernel values by performing a best fit between measured response z(t) and a modeled response y(t). New techniques significantly diminish the exponential growth of the number of computed kernel coefficients at second and third order in order to combat and reasonably alleviate the curse of dimensionality.

In this paper, we draw attention to the requirements for obtaining measurement data essential to accurate modeling and prediction of atmospheric laser beam propagation at heights 10 meters or less above the ocean surface. This is an atmospheric zone we call the marine boundary layer. There are numerous models and applications for optical beam propagation in the upper atmosphere that based on the Kolmogorov Theory of Turbulence. However, close to the ocean surface standard procedures for measuring and modeling the effects of boundary layer turbulence may no longer apply. In addition to classical measurement procedures, we suggest a new methodology and measurement procedures for exploration of beam propagation and the ensuing dynamics within the marine boundary layer.^[1,2]

Salt aerosols will undergo deliquescence as humidity is increased. This deliquescent transition dramatically affects the ability of aerosols to extinguish light. It is known that the relative humidity is very high in the viscous sublayer at the ocean surface (~98%) but decreases to an average of 80% in the surface layer. We present results of an investigation of the impact of inclusions on the deliquescence point and correlated optical properties of salt aerosols.

Estimating the variation in the spectral transmission and scattering of optical and near-IR radiation near the sea surface under a range of conditions should be feasible using historical data collected off the coast of New Hampshire USA and along the coastline in the Gulf of Maine. Presented here are long-term offshore aerosol optical depth measurements collected using an AERONET sun photometer from 2007-2011 and near-surface wind and (3 m) horizontal visibility measurements collected using surface meteorological buoys from 2001-present. Future analysis of these data can address their correlation with near-surface meteorological and sea state conditions and to exploit an intensive but limited subset of historical aerosol particle measurements collected here both during a large research ship surveys (ICARTT) as well as with a dedicated aerosol measurement station in summer 2005. Refractive index variation and relevant altitude-dependent differences in meteorological scalars are also investigated using unique offshore long-term measurements at 3 and 32 m above sea level. Overall project results should provide new information for assessment against several existing models for aerosol extinction in marine environments.