This PDF file contains the front matter associated with SPIE Proceedings Volume 8388, including the Title Page, Copyright information, Table of Contents, and Conference Committee listing.

Currently existing acoustic based Gunfire Detection Systems (GDS) such as soldier wearable, vehicle mounted, and fixed site devices provide enemy detection and localization capabilities to the user. However, the solution to the problem of portability versus performance tradeoff remains elusive. The Data Fusion Module (DFM), described herein, is a sensor/platform agnostic software supplemental tool that addresses this tradeoff problem by leveraging existing soldier networks to enhance GDS performance across a Tactical Combat Unit (TCU). The DFM software enhances performance by leveraging all available acoustic GDS information across the TCU synergistically to calculate highly accurate solutions more consistently than any individual GDS in the TCU. The networked sensor architecture provides additional capabilities addressing the multiple shooter/fire-fight problems in addition to sniper detection/localization. The addition of the fusion solution to the overall Size, Weight and Power & Cost (SWaP&C) is zero to negligible. At the end of the first-year effort, the DFM integrated sensor network's performance was impressive showing improvements upwards of 50% in comparison to a single sensor solution. Further improvements are expected when the networked sensor architecture created in this effort is fully exploited.

The short-wave infrared (SWIR) and mid-wave infrared (MWIR) bands contain atmospheric transmission windows spanning approximately 1.50-1.75 μm and 4.6-4.9 μm, making lasers emitting in these ranges suited for high bandwidth covert free-space optical (FSO) communication. In addition to 1.55 μm lasers, a quantum cascade laser exhibiting room temperature emission at ~4.85 μm has been developed for FSO. Transmission coefficients and losses are simulated using MODTRAN for optical path lengths of up to 2 km to for various atmospheric conditions. By better understanding the effects of turbulence and associated refractive index structure parameter on FSO transmission, measures can be implemented to reduce the bit error rate and increase data throughput, enabling more efficient and accurate communication links. FSO beam optimization is provided through adaptive optics using a deformable mirror and Shack- Hartmann wavefront sensor, whereby wavefront distortion of a transmitted beam is measured and the wavefront is modulated in real time to compensate for the effects of turbulence to provide optimized FSO reception through the evaporation layer. Additionally, a dedicated automatic target recognition and tracking optical correlator system using advanced processing technology has been developed. Rapidly cycling data-cubes with size, shape, and orientation are employed with software algorithms for this system to isolate correlation peaks and enable tracking of targets in maritime environments with future track prediction. Using advanced techniques and compensation, limitations associated with infrared FSO transmission and reception through the evaporation layer may be overcome or circumvented to provide high bandwidth communication through turbulence or adverse weather conditions.

As a rising navigation technology, vision navigation has many advantages, such as passive measurement, antiinterference, no accumulation of error and comprehensive parameters, etc. It shows a promising application prospects in autonomous navigation for UAV. Based on an efficient, reliable and accurate scene matching, a vision altimeter and 3-D position estimation method are proposed. By matching multiple points between aerial image and reference image, it estimates UAV's position and height according to photogrammetry. To measure UAV's velocity, a mapless speed measurement method which tracks ground features between image sequences is introduced. Flight tests had shown the effectiveness and accuracy of our methods.

A very small form-factor ultraviolet (UV) light source is being developed for military and industrial applications. A miniature UV laser source using multi-wavelength Quasi-Phase- Matched QPM technique utilizing optical non-linear crystals such as Lithium Niobate (LN), Lithium Tantalate (LT), and Magnesium Oxide doped LN and LT (MgO:LN and MgO:LT), KTP crystals is being developed as a part of solution to meet small form-factor. The UV source consists of thermo-electric cooler, heat-sink carrier on which two focusing lens-arrays, array of non-linear crystals are mounted. The light source used for this purpose is 808 nm which generates 1064 nm light after passing through the Nd:YVO4 crystal. The 1064 nm light enters through lens to make incident on to a PPLT or PPLN crystal generating the third harmonic generation output of 355 nm. Simulations show how an average power of 1.0 Watt with a small form factor is achieved. Simulation modeling demonstrates that the third harmonic grating for PPLN devices allowing an overall prediction of expected output efficiency. Simulations show the affect of process variation, excitation frequency variation, and temperature affects power output.

We utilize a hemispherical camera to develop a passive mapping algorithm that exploits visibility to the sky. We use this visibility to derive a measure of occupancy to build a wide area volumetric map. This process produces a 3D representation of the environment derived solely from video. The resolution of this map is further refined by active motion of the vehicle. Because this method visually exploits the energy of the sky, its effective range is potentially much higher than an active energy sensor. That is, all the space between the sensor and the far horizon can be effectively cleared. Moreover, since this approach is completely passive (it does not emit energy) it is ideal for missions that require stealth. We present initial results from testing this concept on an autonomous vehicle circumnavigating a series of outdoor buildings.

The Heterostructural Uncooled Magnetic Sensors (HUMS) program sponsored by the Defense Advanced Research Projects Agency (DARPA/DSO) is focused on developing magnetic field sensors that operate at room temperature with an ultra-sensitivity to enable applications such as through-wall imaging, perimeter fences, tagging/tracking, and other man-portable operations. Four teams of researchers are participating in the program, with Virginia Tech and University of Maryland leading multiferroic heterostructural materials development and Princeton University and the National Institute of Standards and Technology (NIST) leading atomic vapor cell development. Leveraging the strengths of these two technologies, each team has made advancements towards the program goal of ground-breaking sensitivity, reduced noise, and portability while operating under room temperature conditions. This paper summarizes the program's achievements so far and highlights the accomplishments made by each team.

A next generation magnetic sensor is being developed at L-3 Communications, Communication Systems East to enhance the ability of Army and Marine Corps unattended ground sensor (UGS) systems to detect and track targets on the battlefield. This paper describes a magnetic sensor that provides superior detection range for both armed personnel and vehicle targets, at a reduced size, weight, and level of power consumption (SWAP) over currently available magnetic sensors. The design integrates the proven technology of a flux gate magnetometer combined with advanced digital signal processing algorithms to provide the warfighter with a rapidly deployable, extremely low false-alarm-rate sensor. This new sensor improves on currently available magnetic UGS systems by providing not only target detection and direction information, but also a magnetic disturbance readout, indicating the size of the target. The sensor integrates with Government Off-the-Shelf (GOTS) systems such as the United States Army's Battlefield Anti-Intrusion System (BAIS) and the United States Marine Corps Tactical Remote Sensor System (TRSS). The system has undergone testing by the US Marine Corps, as well as extensive company testing. Results from these field tests are given.

A sensor node having two types of sensors: sound and seismic units was used for signal collection in a test with different moving light vehicles on a gravel road in a quiet area. An analysis of signals from the node at low frequencies (less than 100 Hz) shows the possibility of tested vehicles detection at long distance. The sound signals for the vehicle motion were detected above the lowest frequencies of 15-20 Hz only while the seismic signals had the maxima in that frequency band. Another test was conducted on the ground to find the common vibrations of a light vehicle and the ground due to vehicle passby in frequencies below 100 Hz. For this signal collection the same sensor node was used. An additional 3-x accelerometer was installed in the vehicle cabin above the transmission. For start time synchronization of recorded signals from the node on the ground and 3-x accelerometer in the vehicle cabin a radio channel was used. Results for this test revealed the vehicle vibrations due to motion were detected on the ground with all three components of the 3-axes geophone for the test track entire distance.

The National Center for Physical Acoustics (NCPA) at the University of Mississippi is working on the application of ultrasonic Doppler sonars in air for personnel motion detection. Two traditional Doppler sonar configurations, a monostatic and a bistatic, are being studied. In the monostatic configuration, the distance between the transmitter and the receiver is small. The proximity of the source to the receiver places a limitation on the system associated with the overloading of the receivers' input due to acoustic energy leakage from the transmitters' output. The maximum range of detection is therefore limited by the dynamic range of the acquisition system. In a bistatic Doppler ultrasonic sonar, the source and receiver are spaced apart and the acoustic energy along the direct path does not constrain the maximum acoustic power level output of the transmitter. In a monostatic configuration the acoustic signal suffers from beam spreading and natural absorption during propagation from the transmitter to the target and from the target back to the receiver. In a bistatic configuration the acoustic propagation is in one direction only and theoretically the detection distance can be twice the monostatic distance. For comparison the experiments of a human walking in a building hallway using the bistatic and monostaic Doppler sonars in air were conducted. The experimental results for human signatures from these Doppler sonars are presented and discussed.

We introduce a novel method of using ground track indicators in conjunction with our Spatial Voting (SV) algorithm and data fusing Data Models to distinguish target types from motion signatures alone. We simulate 3 different types of behaviors: rabbit, coyote, and human. We then apply SV to combine individual position reports obtained via radar track indicators into object tracks that are then characterized using the methods shown in this paper. The features obtained from this characterization are then used as input into a Data Model equation classifier or a look-up table classifier to label the track behavior as either rabbit, coyote, or human. Our results and methods show promise and are presented here.

When unmanned distributed sensor fields are developed for rapid deployment in hostile areas, the deployment may consist of multiple sensor types. This occurs because of the variations in expected threats and uncertainties about the details of the local environmental conditions. As more detailed information is available at deployment, the quantity and types of sensors are given and fixed, yet the specific pattern for the configuration of their deployment is still variable. We develop a new optimization approach for planning these configurations for this resource constrained sensor application. Our approach takes into account the variety of sensors available and their respective expected performance in the environment, as well as the target uncertainty. Due to the large dimensionality of the design space for this unmanned sensor planning problem, heuristic-based optimizations will provide very sub-optimal solutions and gradient-based methods lack a good quality initialization. Instead, we utilize a robust optimization procedure that combines genetic algorithms with nonlinear programming techniques to create numerical solutions for determining the optimal spatial distribution of sensing effort for each type of sensor. We illustrate the effectiveness of the approach on numerical examples, and also illustrate the qualitative difference in the optimal patterns as a function of the relative numbers of available sensors of each type. We conclude by using the optimization results to discuss the benefits of interspersing the different sensor types, as opposed to creating area sub-segmentations for each type.

When acoustic signals are subject to measurement over large distances or extended periods of time, the environmental conditions governing their propagation are unlikely to remain constant over the necessary spatial and temporal extents. Relative to a static environment, such inhomogeneities may result in severe signal distortion, such as non-linear warping, and can significantly degrade subsequent signal processing tasks such as classification and time-delay estimation. In this paper we 1) describe a set of experiments that were performed in order to collect space-time acoustic propagation data for empirical modeling, paying particular attention to important experimental design issues such as optimal sampling rates in the spatial domain, and 2) present a statistical two-dimensional model for inhomogeneous environments that describes the space-time distribution of acoustic propagation velocity governing low-frequency long-range propagation of aeroacoustic signals with long durations (several minutes). The model includes a deterministic component to model structured changes (e.g., increasing temperature during morning hours) and a stochastic component, specified by a two dimensional Gaussian random process, to capture correlated random deviations. Cram´er-Rao bounds are presented as a means of evaluating and optimizing sensor geometries for learning model parameters.

Pearls of Wisdom has been working on the development of sensing systems for unattended ground applications using miniaturized elements. Our vision is to enable various monitoring applications using the deployment of a large number of low cost miniature wireless sensors. The operation of sensors of multiple modalities working in harmony, together with the ability to integrate the measurements and turn them into coherent information provides an opportunity for new applications and improved performance. The development of such systems is based on three pillars of excellence: a wireless network foundation which is low bandwidth, yet flexible enough to support the needs of different applications, ultra-low-power design of sensors and a data analysis system.

We have designed a compact, wireless, GPS-enabled array of inexpensive radiation sensors based on scintillation counting. Each sensor has a scintillator, photomultiplier tube, and pulse-counting circuit that includes a comparator, digital potentiometer and microcontroller. This design provides a high level of sensitivity and reliability. A 0.2 m² PV panel powers each sensor providing a maintenance-free 24/7 energy source. The sensor can be mounted within a roadway light-post and monitor radiological activity along transport routes. Each sensor wirelessly transmits real-time data (as counts per second) up to 2 miles with a XBee radio module, and the data is received by a XBee receive-module on a computer. Data collection software logs the information from all sensors and provides real-time identification of radiation events. Measurements performed to-date demonstrate the ability of a sensor to detect a 20 μCi source at 3.5 meters when packaged with a PVT (plastic) scintillator, and 7 meters for a sensor with a CsI crystal (more expensive but ~5 times more sensitive). It is calculated that the sensor-architecture can detect sources moving as fast as 130 km/h based on the current data rate and statistical bounds of 3-sigma threshold detection. The sensor array is suitable for identifying and tracking a radiation threat from a dirty bomb along roadways.

In this paper we analyze the seismic signals generated by people and animals walking. It is known that when a person walks, the heel strikes first and then the front of the foot; whereas animals walk on their hoofs. This difference in the walking patterns result in significant changes in the seismic signatures for both people and animals. Similarly, men walk differently than women and they also have different weight distributions resulting in different signatures for men and women. They also have different cadence or gait patterns. We distinguish the significant features in seismic signatures to distinguish people and animals. Ultrasonic Doppler returns capture the variations in the gait. The Doppler returns will be analyzed to distinguish people and animals. Algorithms to classify the signatures will be provided. The algorithms will be tested on the data collected at a horse farm with women, men and people walking. The results will be discussed along with possible future research directions to reduce the number of false alarms.

Surveillance is normally performed by humans, since it requires visual intelligence. However, this can be dull and dangerous, especially for military operations. Therefore, unmanned autonomous visual-intelligence systems are desired. In this paper, we present a novel system that can recognize human actions, which are relevant to detect operationally significant activity. Central to the system is a break-down of high-level perceptual concepts (verbs) in simpler observable events. The system is trained on 3482 videos and evaluated on 2589 videos from the DARPA Mind's Eye program, with for each video human annotations indicating the presence or absence of 48 different actions. The results show that our system reaches good performance approaching the human average response.

There are many separated infrastructural objects within a harbor area that may be considered "critical", such as gas and oil terminals or anchored naval vessels. Those objects require special protection, including security systems capable of monitoring both surface and underwater areas, because an intrusion into the protected area may be attempted using small surface vehicles (boats, kayaks, rafts, floating devices with weapons and explosives) as well as underwater ones (manned or unmanned submarines, scuba divers). The paper will present the concept of multisensor security system for a harbor protection, capable of complex monitoring of selected critical objects within the protected area. The proposed system consists of a command centre and several different sensors deployed in key areas, providing effective protection from land and sea, with special attention focused on the monitoring of underwater zone. The initial project of such systems will be presented, its configuration and initial tests of the selected components. The protection of surface area is based on medium-range radar and LLTV and infrared cameras. Underwater zone will be monitored by a sonar and acoustic and magnetic barriers, connected into an integrated monitoring system. Theoretical analyses concerning the detection of fast, small surface objects (such as RIB boats) by a camera system and real test results in various weather conditions will also be presented.

Seismic Unattended Ground Sensors (UGS) are low cost and covert, making them a suitable candidate for border patrol. Current seismic UGS systems use cadence-based intrusion detection algorithms and are easily confused between humans and animals. The poor discrimination ability between humans and animals results in missed detections as well as higher false (nuisance) alarm rates. In order for seismic UGS systems to be deployed successfully, new signal processing algorithms with better discrimination ability between humans and animals are needed. We have characterized the seismic signals using frequency domain and time-frequency domain statistics, which improve the discrimination between humans, animals and vehicles.

A key challenge of sentry and monitoring duties is detection of approaching people in areas of little human traffic. We are exploring smartphones as easily available, easily portable, and less expensive alternatives to traditional military sensors for this task, where the sensors are already integrated into the package. We developed an application program for the Android smartphone that uses its sensors to detect people passing nearby; it takes their pictures for subsequent transmission to a central monitoring station. We experimented with the microphone, light sensor, vibration sensor, proximity sensor, orientation sensor, and magnetic sensor of the Android. We got best results with the microphone (looking for footsteps) and light sensor (looking for abrupt changes in light), and sometimes good results with the vibration sensor. We ran a variety of tests with subjects walking at various distances from the phone under different environmental conditions to measure limits on acceptable detection. We got best results by combining average loudness over a 200 millisecond period with a brightness threshold adjusted to the background brightness, and we set our phones to trigger pictures no more than twice a second. Subjects needed to be within ten feet of the phone for reliable triggering, and some surfaces gave poorer results. We primarily tested using the Motorola Atrix 4G (Android 2.3.4) and HTC Evo 4G (Android 2.3.3) and found only a few differences in performance running the same program, which we attribute to differences in the hardware. We also tested two older Android phones that had problems with crashing when running our program. Our results provide good guidance for when and where to use this approach to inexpensive sensing.

Integrated security systems are essential to pre-empting criminal assaults. Nearly 500,000 sites have been identified (source: US DHS) as critical infrastructure sites that would suffer severe damage if a security breach should occur. One major breach in any of 123 U.S. facilities, identified as "most critical", threatens more than 1,000,000 people. The vulnerabilities of critical infrastructure are expected to continue and even heighten over the coming years.