This PDF file contains the front matter associated with SPIE Proceedings Volume 6963, including the Title Page, Copyright information, Table of Contents, Introduction, and the Conference Committee listing.

The well understood lack of a 'silver bullet' sensor technology which can provide everything wanted from Intelligence, Surveillance, Target Acquisition and Reconnaissance (ISTAR) when using a single sensor type on a single platform, combined with the improved ability to network multiple platforms together is at the heart of the growth in networkcentric ISTAR. When this is linked to the growth in data storage capacity then a much richer and more beneficial opportunity for the transformation of network-centric ISTAR opens out. In particular the long term storage of sensor data (at detection or pre-detection points in the sensor processing chain) enables the traditional one-way data fusion (or signal processing) approach to be turned into a much richer two-way (or bi-directional) chain of adaptive processes where higher level context is used routinely, hypothesis testing is the norm and the system can report on both the positive presence and absence of 'targets' of interest. Finally the paper discusses some of the key challenges to be overcome if the potential advantages of fully networked bidirectional adaptive signal and data processing are to be realised.

In this paper we overview the nonlinear matched filtering for photon counting recognition with 3D passive sensing. The first and second order statistical properties of the nonlinear matched filtering can improve the recognition performance compared to the linear matched filtering. Automatic target reconstruction and recognition are addressed for partially occluded objects. The recognition performance is shown to be improved significantly in the reconstruction space. The discrimination capability is analyzed in terms of Fisher ratio (FR) and receiver operating characteristic (ROC) curves.

Unattended Ground Sensors (UGS) have recently gained momentum in the military for applications such as force protection and perimeter surveillance. Many of these unattended ground sensors are deployed in theater today across multiple divisions of the military. In addition to UGS needs, there is a growing need for communication capabilities down to the individual soldier. The majority of UGS systems require specialized devices to monitor intrusion activities. This causes added burden to the soldiers who have to carry a multitude of equipment for a particular mission. To eliminate the need for a specialized device, some systems send sensor data to an Intelligence Operations Center. However, even though an extra device has been eliminated, there are concerns with delays and latency to disseminate sensor data down to the reactionary force as actionable reports. The RF Communications Division of Harris Corporation has developed a family of UGS equipment that provides seamless integration with currently fielded tactical communications architectures. This paper provides an overview of how this equipment eliminates the need of a separate monitoring device by providing direct actionable intelligence to a reactionary force to already fielded tactical radios as well as enhanced situational awareness up through the Tactical Radio Communications Architecture.

McQ's OmniSense® Unattended Ground Sensor (UGS) System has been deployed in large numbers to support current DOD warfighting efforts. This networked UGS system connects the user to the remotely deployed sensors to receive target information and to allow a user to remotely reconfigure the sensors. These intelligent sensors detect and classify the targets, in addition to, capturing a picture of the target. The ability to geographically distribute both the users and the sensors is based on using a network oriented common data structure. McQ developed and has implemented for tactical DOD use the Common Data Interchange Format (CDIF) sensor language. This has enabled UGS to be networked over NIPRnet and SIPRnet links so that operators in the field, at Forward Operating Bases, at Tactical Operations Centers, and at Command Centers can simultaneously share the data. The Army Research Laboratory has further enhanced and extended this network architecture by integrating a common radio (Blue Radio) and demonstrating in Army C4ISR exercises that UGS systems from multiple vendors can be integrated into the Future Combat System FBCB2 situation awareness capability. McQ has extended its OmniSense® UGS capability with direct network connectivity to the soldier, long range standoff imagers controlled over the network, terrestrial network relays, and with a new low cost OmniSenseCOR^TM sensor. McQ will present an overview of the technology provided by the OmniSense® UGS system.

This paper addresses benefits derived from the universal gateway utilized in Northrop Grumman Systems Corporation's (NGSC) SCORPION, a persistent surveillance and target recognition system produced by the Xetron campus in Cincinnati, Ohio. SCORPION is currently deployed in Operations Iraqi Freedom (OIF) and Enduring Freedom (OEF). The SCORPION universal gateway is a flexible, field programmable system that provides integration of over forty Unattended Ground Sensor (UGS) types from a variety of manufacturers, multiple visible and thermal electro-optical (EO) imagers, and numerous long haul satellite and terrestrial communications links, including the Army Research Lab (ARL) Blue Radio. Xetron has been integrating best in class sensors with this universal gateway to provide encrypted data exfiltration and remote sensor command and control since 1998. SCORPION data can be distributed point to point, or to multiple Common Operational Picture (COP) systems, including Command and Control Personal Computer (C2PC), Common Data Interchange Format for the Situational Awareness Display (CDIF/SAD), Force XXI Battle Command Brigade and Below (FBCB2), Defense Common Ground Systems (DCGS), and Remote Automated Position Identification System (RAPIDS).

Technology advancements for the USMC UGS system are described. Integration of the ARL Blue Radio/CSR into the System Controller and Radio Repeater permit the TRSS system to operate seamlessly within the Family of UGS concept. In addition to the Blue Radio/CSR, the TRSS system provides VHF and SATCOM radio links. The TRSS system is compatible with a wide range of imagers, including those with both analog and digital interfaces. The TRSS System Controller permits simultaneous monitoring of 2 camera inputs. To complement enhanced compatibility and improved processing, the mechanical housing of the TRSS System Controller has been updated. The SDR-II, a system monitoring device, also incorporates four Blue Radio/CSRs along with other communication capabilities, making it an ideal choice for a monitoring station within the Family of UGS. Field testing of L-3 Nova's UGS system at YPG has shown flawless performance, capturing all 126 targets.

This paper describes a distributed sensor network for a coastal maritime security system. This concept incorporates a network of small passive and active multi-phenomenological unattended sensors and shore based optical sensors to detect, classify, and track submerged threat objects approaching high value coastal assets, such as ports, harbors, residential, commercial, and military facilities and areas. The network of unattended, in-water sensors perform the initial detection, classification, and coarse tracking and then queues shore based optical laser radar sensors. These shore-based sensors perform a queued sector search to develop a refined track on the submerged threat objects that were initially detected by the unattended sensor network. Potential threat objects include swimmers, small unmanned underwater vehicles (UUV's), small submarines, and submerged barges. All of these threats have the potential to transport threat objects such as explosives, chemical, biological, radiological, and nuclear materials. Reliable systems with low false alarm rates (FAR) are proposed. Tens to hundreds of low cost passive sensors are proposed to be deployed conjunctively with several active acoustic and optical sensors in threat and facility dependant patterns to maximize the detection, tracking and classification of submerged threat objects. The integrated command and control system and novel microbial fuel cells to power these sensor networks are also described.

The detection and localization of hostile weapons firing has been demonstrated successfully with acoustic sensor arrays on unattended ground sensors (UGS), ground-vehicles, and unmanned aerial vehicles (UAVs). Some of the more mature systems have demonstrated significant capabilities and provide direct support to ongoing counter-sniper operations. The Army Research Laboratory (ARL) is conducting research and development for a helmet-mounted system to acoustically detect and localize small arms firing, or other events such as RPG, mortars, and explosions, as well as other non-transient signatures. Since today's soldier is quickly being asked to take on more and more reconnaissance, surveillance, & target acquisition (RSTA) functions, sensor augmentation enables him to become a mobile and networked sensor node on the complex and dynamic battlefield. Having a body-worn threat detection and localization capability for events that pose an immediate danger to the soldiers around him can significantly enhance their survivability and lethality, as well as enable him to provide and use situational awareness clues on the networked battlefield. This paper addresses some of the difficulties encountered by an acoustic system in an urban environment. Complex reverberation, multipath, diffraction, and signature masking by building structures makes this a very harsh environment for robust detection and classification of shockwaves and muzzle blasts. Multifunctional acoustic detection arrays can provide persistent surveillance and enhanced situational awareness for every soldier.

The detection and localization of small fire arms is envisaged by use of acoustic devices. This paper describes the capability to detect and localize snipers in open field and in urban conditions. This work was performed by ISL and DGA during various national and NATO trials. During recent military conflicts, as well as for security interventions, the urban zone has taken a prominent place. Experimental results measured in free-field conditions, compared with those measured in a village used for military training, show that the streets and houses can generate many reflections of the original gunshot, requiring new signal processing techniques to separate each contribution. For this purpose a specific numerical model has been developed. A few examples of experimental and numerical results obtained for the validation of this methodology will be presented.

Feature extraction methods based on the statistical analysis of the change in event pressure levels over a period and the level of ambient pressure excitation facilitate the development of a robust classification algorithm. The features reliably discriminates mortar and artillery variants via acoustic signals produced during the launch events. Utilizing acoustic sensors to exploit the sound waveform generated from the blast for the identification of mortar and artillery variants as type A, etcetera through analysis of the waveform. Distinct characteristics arise within the different mortar/artillery variants because varying HE mortar payloads and related charges emphasize varying size events at launch. The waveform holds various harmonic properties distinct to a given mortar/artillery variant that through advanced signal processing and data mining techniques can employed to classify a given type. The skewness and other statistical processing techniques are used to extract the predominant components from the acoustic signatures at ranges exceeding 3000m. Exploiting these techniques will help develop a feature set highly independent of range, providing discrimination based on acoustic elements of the blast wave. Highly reliable discrimination will be achieved with a feed-forward neural network classifier trained on a feature space derived from the distribution of statistical coefficients, frequency spectrum, and higher frequency details found within different energy bands. The processes that are described herein extend current technologies, which emphasis acoustic sensor systems to provide such situational awareness.

Explosion detection and recognition is a critical capability to provide situational awareness to the war-fighters in battlefield. Acoustic sensors are frequently deployed to detect such events and to trigger more expensive sensing/sensor modalities (i.e. radar, laser spectroscope, IR etc.). Acoustic analysis of explosions has been intensively studied to reliably discriminate mortars, artillery, round variations, and type of blast (i.e. chemical/biological or high-explosive). One of the major challenges is high level of noise, which may include non-coherent noise generated from the environmental background and coherent noise induced by possible mobile acoustic sensor platform. In this work, we introduce a new acoustic scene analysis method to effectively enhance explosion classification reliability and reduce the false alarm rate at low SNR and with high coherent noise. The proposed method is based on acoustic signature modeling using Hidden Markov Models (HMMs). Special frequency domain acoustic features characterizing explosions as well as coherent noise are extracted from each signal segment, which forms an observation vector for HMM training and test. Classification is based on a unique model similarity measure between the HMM estimated from the test observations and the trained HMMs. Experimental tests are based on the acoustic explosion dataset from US ARMY ARDEC, and experimental results have demonstrated the effectiveness of the proposed method.

The ShotSpotter Gunshot Location System® has a flexible architecture that employs a wireless network of sensors mounted on buildings, vehicles, or soldiers. These distributed arrays with redundant acoustic paths combine audio time of arrival and/or angle of arrival from multiple sensors to calculate locations in challenging environments with obstructions or reflections. Muzzle and bullet sounds can be used depending on the proximity of the sensors to the bullet trajectory. Large array geometries allow not only close-range sniper detection but also wide-area situational awareness of enemy weapon activity. Examples of acoustic detections are presented in this paper using data from a combination of fixed and mobile sensors.

Statistics-based characterizations of acoustic propagation, namely, fading and coherence, are being developed as functions of urban terrain zones. The fading and coherence curves are characterized for each of several urban terrain zones of interest, and the resulting curves are parameterized as a function of frequency and distance from the source. With the parameters for signal fading and coherence as a function of frequency, distance to source, and urban terrain zone type, the decision support tool SPEBE (Sensor Performance Evaluator for Battle-space Environments) is extended to urban areas. Combined with a separate effort characterizing background noise levels as functions also of urban terrain zones, a tool for predicting probability of detection for various sources in urban areas is demonstrated.

Future US Army ground sensors in urban terrain will process acoustic signals to detect, classify, and locate sources of interest. Optimal processing will require understanding of the effects of the urban infrastructure on sound propagation. These include multi-path phenomena that must be accounted for in sensor placement and performance algorithms. This work applies Fourier analysis to urban acoustic wave-field data from three-dimensional high-performance computations to generate statistical measures of signal fading caused by scattering. The work calculates these measures from ratios of Fourier transforms of wave-field signals with and without scattering to isolate the structure-induced scattering.

This paper details the development of a modularized system level model of a sensor whose detector dimensions may be small with respect to the distance between adjacent detectors. The effects of individual system components and characteristics such as target to background properties, collection optics, detectors, and classifiers will be modeled. These individual effects will then be combined to provide an overall system performance model. The model will facilitate design trade offs for Unattended Ground Sensors. The size and power restrictions of these sensors often preclude these sensors from being effective in high resolution applications such as target identification. Consequently, existing imager performance models are not directly applicable. However, these systems are well suited for applications such as broad scale classifications or differentiations between targets such as humans, animals or small vehicles. Furthermore, these sensors do not have to be spaced closely together to be effective in these applications. Therefore, the demand for these sensors is increasing for both the military and homeland security.

This paper presents a simple prototype sparse detector imaging sensor built using sixteen off-the-shelf, retro-reflective, infrared-sensing elements placed at five-inch intervals in a vertical configuration. Profiling experiments for broad-scale classification of objects, such as humans, humans wearing large backpacks, and humans wearing small backpacks were conducted from data acquired from the sensor. Empirical analysis on models developed using fusion of various classifiers based on a diversity measure shows over ninety percent (90.07%) accuracy (using 10-fold cross validation) in categorizing sensed data into specific classes of interest, such as, humans wearing a large backpack. The results demonstrate that shadow images of sufficient resolution can be captured by the sensor such that broad-scale classification of objects is feasible. The sensor appears to be a low-cost alternative to traditional, high-resolution imaging sensors, and, after industrial packaging, it may be a good candidate for deployment in vast geographic regions in which low-cost, unattended ground sensors with highly accurate classification algorithms are needed.

A persistent problem with real-time video tracking on small UAVs (SUAV) is that tracks can break due to camera motion, target occlusions, frame-to-frame mis-registrations, signal-to-noise ratio issues, and other causes. Repairing video tracks, by appropriately connecting broken tracks, is essential for high quality track maintenance. Previously, we developed a video track repair algorithm (VTR) to repair short track breaks. We investigated performance on tracking data from a video tracker operating on video data acquired from an SUAV. The repair approach accumulates evidence across frames using multihypothesis sequential probability ratio tests (MHSPRT). The MHSPRT framework propagates posterior probabilities associated with each track repair hypothesis to make track connections. In this paper we perform several numerical experiments using simulated tracking data to map out qualitative behavior of the VTR over a wider set of operating conditions. We examine effects of measurement noise level, track state-space separation, number of evidence accumulation frames, and stopping probability threshold on repair performance. We investigate the effect of these factors on posterior probability propagation in the MHSPRT. We indicate potential algorithm enhancements resulting from conclusions drawn from experimental results. We demonstrate how a multi-frame evidence accumulation approach can provide superior performance to a single-frame maximum likelihood approach. We demonstrate that with fewer frames MHSPRT performance compares favorably with maximum a posteriori performance, despite few analytical results on MHSPRT optimality. First we provide an overview of the track repair algorithm. Next we describe the numerical experiments, present results, and interpret results to infer performance behavior.

Target images are very important for evaluating the situation when Unattended Ground Sensors (UGS) are deployed. These images add a significant amount of information to determine the difference between hostile and non-hostile activities, the number of targets in an area, the difference between animals and people, the movement dynamics of targets, and when specific activities of interest are taking place. The imaging capabilities of UGS systems need to provide only target activity and not images without targets in the field of view. The current UGS remote imaging systems are not optimized for target processing and are not low cost. McQ describes in this paper an architectural and technologic approach for significantly improving the processing of images to provide target information while reducing the cost of the intelligent remote imaging capability.

An application is proposed for detection and classification of battlefield ground vehicles using the emitted acoustic signal captured at individual sensor nodes of an ad hoc Wireless Sensor Network (WSN). We make use of the harmonic characteristics of the acoustic emissions of battlefield vehicles, in reducing both the computations carried on the sensor node and the transmitted data to the fusion center for reliable and effcient classification of targets. Previous approaches focus on the lower frequency band of the acoustic emissions up to 500Hz; however, we show in the proposed application how effcient discrimination between battlefield vehicles is performed using features extracted from higher frequency bands (50 - 1500Hz). The application shows that selective time domain acoustic features surpass equivalent spectral features. Collaborative signal processing is utilized, such that estimation of certain signal model parameters is carried by the sensor node, in order to reduce the communication between the sensor node and the fusion center, while the remaining model parameters are estimated at the fusion center. The transmitted data from the sensor node to the fusion center ranges from 1 ~ 5% of the sampled acoustic signal at the node. A variety of classification schemes were examined, such as maximum likelihood, vector quantization and artificial neural networks. Evaluation of the proposed application, through processing of an acoustic data set with comparison to previous results, shows that the improvement is not only in the number of computations but also in the detection and false alarm rate as well.

This paper addresses a new type of sensor that we are calling a profiling sensor. The profiling sensor collects sufficient information to classify objects reliably. In addition to meeting the classification requirement, the profiling sensor is inexpensive to build and maintain, will have a low unit production cost, uses a small amount of power, has a long battery life, and is very low weight. A variety of concepts are presented along with human signatures from one specific profiling sensor. In addition the paper includes a discussion of different concepts or architectures for profiling sensors as a function of potential applications.

This paper proposes an innovative approach for multi-objects recognition for homeland security and defense based intelligent sensor networks. Unlike the conventional way of information analysis, data mining in such networks is typically characterized with high information ambiguity/uncertainty, data redundancy, high dimensionality and real-time constrains. Furthermore, since a typical military based network normally includes multiple mobile sensor platforms, ground forces, fortified tanks, combat flights, and other resources, it is critical to develop intelligent data mining approaches to fuse different information resources to understand dynamic environments, to support decision making processes, and finally to achieve the goals. This paper aims to address these issues with a focus on multi-objects recognition. Instead of classifying a single object as in the traditional image classification problems, the proposed method can automatically learn multiple objectives simultaneously. Image segmentation techniques are used to identify the interesting regions in the field, which correspond to multiple objects such as soldiers or tanks. Since different objects will come with different feature sizes, we propose a feature scaling method to represent each object in the same number of dimensions. This is achieved by linear/nonlinear scaling and sampling techniques. Finally, support vector machine (SVM) based learning algorithms are developed to learn and build the associations for different objects, and such knowledge will be adaptively accumulated for objects recognition in the testing stage. We test the effectiveness of proposed method in different simulated military environments.

Temporal coherence is an important property of many acoustic signals. This paper discusses two fluctuation-based signal processors that improve the temporal coherence of phase and amplitude. Then they exploit the improved coherences to achieve substantial gains, such as, elimination of all noise to achieve exceptionally large "noise-free" automatic detections of temporally coherent signals. Both processors are discussed. One exploits phase fluctuations and the other one exploits amplitude fluctuations. The exploited parameters and signal processors are defined. Results are presented for automatic signal detection of a heavy treaded / tracked vehicle, a helicopter, a fast-boat in shallow coastal water, and a submerged source in the ocean.

The coherence function between the microphone and vertical geophone is investigated for air-coupled and mechanically-coupled sources and offers new insights into air vs. ground source discrimination. This aids one in the understanding of air-coupled sounds from airborne and ground sources and mechanically-coupled vibrations from ground sources. Air borne sources provide energy that that is measured by microphone and, as this energy is coupled into the ground, by geophone. This measured energy, obtained by using co-located sensors (microphone and geophone), will have common amplitude and phase information Ground sources produce mechanically-coupled ground waves that arrive at the geophone with unique amplitude and phase information, independent of any acoustic signal they may radiate. Data analyzed at an Army test site is compared to experimental results.

Seismic methods for footstep detection exploit low frequency vibration waves, typically below 100 Hz. There are two limiting factors for detection of human footsteps at these frequencies: walking styles and the background noise floor. The walking style changes the dynamic footstep force on the ground and, therefore, limits the maximum distance at which walkers may be detected. For seismic frequencies, the background vibration noise floor is higher in urban areas than in quiet areas. This article presents and discusses test results of human footstep measurements as a function of distance using the seismic method in quiet and urban areas.

Unattended ground sensors (UGS) are routinely used to collect intelligence, surveillance, and reconnaissance (ISR) information. Unattended ground sensors consisting of microphone array and geophone are employed to detect rotary wing aircraft. This paper presents an algorithm for the detection of helicopters based on a fusion of rotor harmonics and acoustic-seismic coupling. The main rotor blades of helicopters operate at a fixed RPM to prevent stalling or mechanical damage. In addition, the seismic spectrum is dominated by the acoustic-seismic coupling generated by these rotors; much more so than ground vehicles and other targets where mechanical coupling and a more broadband acoustic source are strong factors. First, an autocorrelation detection method identifies the constant fundamental generated by the helicopter main rotor. Second, key matching frequencies between the acoustic and seismic spectrum are used to locate possible coupled components. Detection can then be based on the ratio of the coupled seismic energy to total seismic energy. The results of the two methods are fused over a few seconds time to provide an initial and continued detection of a helicopter within the sensor range. Performance is measured on data as a function of range and sound pressure level (SPL).

The ability to perform generalized ground vehicle classification by unattended ground sensors (UGS) is an important facet of data analysis performed by modern unattended sensor systems. Large variation in seismic signature propagation from one location to another renders exploiting seismic measurements to classify vehicles a significant challenge. This paper presents the results of using an adaptive methodology to distinguish between tracked and wheeled ground vehicle mobility mechanisms. The methodology is a passive in-situ learning process that does not rely upon an explicit calibration process but does require an estimated range to the target. Furthermore, the benefits of the seismic feature adaptation are realized with a sparse information set. There exist scenarios in which the adaptation fails to provide information when implemented as an independent process. These situations, however, may be mitigated by sharing information with other classification algorithms. Once properly initialized, the in-situ adaptation process correctly categorizes over 95% of ground vehicles.

McQ developed for the U.S. Army Research Laboratory (ARL) a very low-cost iScout® sensor system for detecting people in buildings and caves after military clearing operations to prevent their reuse by adversaries. The mission applications have expanded to include typical field operations such as Force Protection and facility security. To meet a broader mission capability, McQ significantly enhanced the performance of the iScout® Unattended Ground Sensor (UGS) system. The enhanced performance includes improvements to the seismic, acoustic, magnetic, and passive infrared sensor processing algorithms and multimodal fusion to improve target classification. Additional features are a new radio frequency (RF) network architecture, built-in global positioning system (GPS) for automatic sensor position reporting, a new rugged watertight case, and an extremely low power consumption electronics design. McQ will describe these enhancements and present data characterizing the performance of the enhanced iScout® sensors.

Over the past decade, technological advances have enabled the use of increasingly intelligent systems for battlefield surveillance. These systems are triggered by a combination of external devices including acoustic and seismic sensors. Such products are mainly used to detect vehicles and personnel. These systems often use infra-red imagery to record environmental information, but Textron Defense Systems' Terrain Commander is one of a small number of systems which analyze these images for the presence of targets. The Terrain Commander combines acoustic, infrared, magnetic, seismic, and visible spectrum sensors to detect nearby targets in military scenarios. When targets are detected by these sensors, the cameras are triggered and images are captured in the infrared and visible spectrum. In this paper we discuss a method through which such systems can perform target tracking in order to record and transmit only the most pertinent surveillance images. This saves bandwidth which is crucial because these systems often use communication systems with throughputs below 2400bps. This method is expected to be executable on low-power processors at frame rates exceeding 10HZ. We accomplish this by applying target activated frame capture algorithms to infra-red video data. The target activated frame capture algorithms combine edge detection and motion detection to determine the best frames to be transmitted to the end user. This keeps power consumption and bandwidth requirements low. Finally, the results of the algorithm are analyzed.

In this paper we discuss a random walk model to characterize the pulse discharge battery process. Several theoretical results are derived including the mean and variance of an unattended battery-driven sensor lifetime. Some numerical results are presented.

Biomimetic signal processing that is functionally similar to that performed by the mammalian peripheral auditory system consists of several stages. The concatenated stages of the system each favor differing types of hardware implementations. Ideally, the front-end would be an implementation of the mammalian cochlea, which is a tapered nonlinear, traveling-wave amplifier. It is not a good candidate for standard digital implementations. The AM demodulator can be implemented using digital or analog designs. The Automatic Gain Control (AGC) stage is highly unusual. It requires filtering and multiplication in a closed-loop configuration, with bias added at each of two concatenated stages. Its implementation is problematic in DSP, FPGA, full custom digital VLSI, and analog VLSI. The one-bit A/D (also called the "spiking neuron"), while simple at face value, involves a complicated triggering mechanism, which is amenable to DSP, FPGA, and custom digital but computationally intense, and is suited to an analog VLSI implementation. Currently, we have several hardware embodiments of the biomimetic system. The RedOwl application occupies about 160 cubic inches in volume at the present time. A DSP approach can compute 15 channels for two ears for three A/D categories using Analog Devices Tiger SHARC-201 DSP chips within a system size estimated to be on the order of 30 cubic inches. BioMimetic Systems, Inc., a Boston University startup company is developing an FPGA solution. Within the university, we are also pursuing both a custom digital ASIC route and a current-mode analog ASIC.

This contribution is orientated towards the area of the development of new Unattended Ground Sensor - UGS for an urbanized battlefield. Performing operations in a built-up area limits the movement, maneuvering and controlling of armed forces significantly. To protect one's own units, a dedicated communication network is often created with many inputs in the form of sensors with process units and with information which can be distributed to every soldier and commander on the battlefield. The used as well as future UGS are being developed in the sense of "Smart Dust" Technology. The first part of the contribution deals with the design of a miniature UGS which is to be used in an urbanized battlefield area. The next part then discusses the utilization of the properties of the human body as a possible source of feeding of the personal warning equipment and, at the same time, as a medium for data transmission. The conclusion of the contribution describes the results reached in the design of warning equipment where the human body serves both for its feeding and data transmission.

This paper describes the flow of scientific and technological achievements beginning with a stationary "small, smart, biomimetic acoustic processor" designed for DARPA that led to a program aimed at acoustic characterization and direction finding for multiple, mobile platforms. ARL support and collaboration has allowed us to adapt the core technology to multiple platforms including a Packbot robotic platform, a soldier worn platform, as well as a vehicle platform. Each of these has varying size and power requirements, but miniaturization is an important component of the program for creating practical systems which we address further in companion papers. We have configured the system to detect and localize gunfire and tested system performance with live fire from numerous weapons such as the AK47, the Dragunov, and the AR15. The ARL-sponsored work has led to connections with Natick Labs and the Future Force Warrior program, and in addition, the work has many and obvious applications to homeland defense, police, and civilian needs.

In this paper a real-time sound source localizing system is proposed, which is based on previously developed mammalian auditory models. Traditionally, following the models, which use interaural time delay (ITD) estimates, the amount of parallel computations needed by a system to achieve real-time sound source localization is a limiting factor and a design challenge for hardware implementations. Therefore a new approach using a time-shared architecture implementation is introduced. The proposed architecture is a purely sample-base-driven digital system, and it follows closely the continuous-time approach described in the models. Rather than having dedicated hardware on a per frequency channel basis, a specialized core channel, shared for all frequency bands is used. Having an optimized execution time, which is much less than the system's sample rate, the proposed time-shared solution allows the same number of virtual channels to be processed as the dedicated channels in the traditional approach. Hence, the time-shared approach achieves a highly economical and flexible implementation using minimal silicon area. These aspects are particularly important in efficient hardware implementation of a real time biomimetic sound source localization system.

Innovations may lead to magnetic sensors with superior performance. Examples of this are the chip scale atomic magnetometer, magnetic tunnel junctions with MgO barriers, and a device for minimizing the effect of 1/f noise, the MEMS flux concentrator. In the chip scale atomic magnetometer, researchers have been able to fabricate the light source, optics, heater, optical cell, and photodiode detector in a stack that passes through a silicon wafer. Theoretical and subsequent experimental work has led to the observation of magnetoresistance values of 400% at room temperature in magnetic tunnel junctions with MgO barriers. The MEMS flux concentrator has the potential to increase the sensitivity of magnetic sensors at low frequencies by more than an order of magnitude. The MEMS flux concentrator does this by shifting the operating frequency to higher frequencies where the 1/f noise is much smaller. The shift occurs because the motion of flux concentrators on MEMS flaps modulates the field at kHz frequencies at the position of the sensor.

Recently, we have shown the emergence of oscillations in overdamped undriven nonlinear dynamic systems subject to carefully crafted coupled schemes and operating conditions. Here, we summarize these results for a system of N = 3 coupled ferromagnetic cores, the underpinning of a "coupled-core fluxgate magnetometer" (CCFM): the oscillatory behaviour is triggered when the coupling constant exceeds a threshold value (bifurcation point), and the oscillation frequency exhibits a characteristic scaling behaviour with the "separation" of the coupling constant from its threshold value, as well as with an external "target" dc magnetic flux signal. We present assorted performance figures of the magnetometer, and also describe figure of merit (to characterize the device) that includes the effects of the sensor noise-floor as well its sensitivity.

The sensor community has long been presented with the problem of prioritizing among several competing sensor system variables due to the inability to produce a high confidence, low-cost, reliable, and compact device. Typically a solution for very critical scenarios has been a high-cost scale reduction of larger laboratory based instrumentation. This often produced data on a single parameter that is beyond reproach, however this can also produce a very delicate, bulky, and costly system often requiring a vacuum system of some sort. An alternative approach involves using micro-electro-mechanical systems (MEMS) based sensors. This typically results in low-cost and extremely compact devices that often produce dubious or insufficient data. Our approach integrates multiple orthogonal stimuli within a single chip to produce a MEMS based sensor that has a very high degree of signal confidence. Each individual sensed parameter may not produce very high-confidence data, but the combination of multiple independent parameters significantly improves detection reliability in a small low-cost package. In this work we address the integration of THz to our traditional MEMS based IR sensor elements. Also it is very significant that we can now produce IR images at atmospheric pressures which enables the integration of chemical and biological sensing within the same MEMS array.

The Buckeye high-resolution geospatial collection system is currently supporting operations within both Iraq and Afghanistan. The Buckeye system, originally developed by the U.S. Army Corps of Engineers (USACE), Engineer Research and Development Center (ERDC), provides timely tactical high resolution geospatial information to field commanders. The Buckeye system is applicable in the following arenas: intelligence, surveillance, and reconnaissance (ISR), mapping, change detection, mission rehearsal, simulation, and battlefield visualization. Three distinct Buckeye systems hosted on multiple air platforms have provided continuous geospatial data delivery to U.S. Forces since November 2004. Further capability is to be provided by integrating next generation Buckeye components to an Unmanned Aerial Vehicle (UAV). The UAV selected for this effort is the experimental Arrow Unmanned Aerial System (UAS). This paper describes the physical and systems integration of the Buckeye Electro-Optical (EO) and Light Detection and Ranging (LIDAR) components to the Arrow platform. Engineering solutions for mass balancing, thermal dispersion, and component calibration are presented. The distributed on-board architecture which performs instrument control, image compression, and data downlink, is described and discussed. Finally theoretical, laboratory and flight testing results are presented with a discussion on implementation and data dissemination within a tactical environment.

The experimental Automated Ship Image Acquisition System (ASIA) collects high-resolution ship photographs at a shore-based laboratory, with minimal human intervention. The system uses Automatic Identification System (AIS) data to direct a high-resolution SLR digital camera to ship targets and to identify the ships in the resulting photographs. The photo database is then searchable using the rich data fields from AIS, which include the name, type, call sign and various vessel identification numbers. The high-resolution images from ASIA are intended to provide information that can corroborate AIS reports (e.g., extract identification from the name on the hull) or provide information that has been omitted from the AIS reports (e.g., missing or incorrect hull dimensions, cargo, etc). Once assembled into a searchable image database, the images can be used for a wide variety of marine safety and security applications. This paper documents the author's experience with the practicality of composing photographs based on AIS reports alone, describing a number of ways in which this can go wrong, from errors in the AIS reports, to fixed and mobile obstructions and multiple ships in the shot. The frequency with which various errors occurred in automatically-composed photographs collected in Halifax harbour in winter time were determined by manual examination of the images. 45% of the images examined were considered of a quality sufficient to read identification markings, numbers and text off the entire ship. One of the main technical challenges for ASIA lies in automatically differentiating good and bad photographs, so that few bad ones would be shown to human users. Initial attempts at automatic photo rating showed 75% agreement with manual assessments.

The U.S. Army Research Laboratory (ARL) Multimodal Signatures Database (MMSDB) is a centralized collection of sensor data of various modalities that are co-located and co-registered. The signatures include ground and air vehicles, personnel, mortar, artillery, small arms gunfire from potential sniper weapons, explosives, and many other high value targets. This data is made available to Department of Defense (DoD) and DoD contractors, Intel agencies, other government agencies (OGA), and academia for use in developing target detection, tracking, and classification algorithms and systems to protect our Soldiers. A platform independent Web interface disseminates the signatures to researchers and engineers within the scientific community. Hierarchical Data Format 5 (HDF5) signature models provide an excellent solution for the sharing of complex multimodal signature data for algorithmic development and database requirements. Many open source tools for viewing and plotting HDF5 signatures are available over the Web. Seamless integration of HDF5 signatures is possible in both proprietary computational environments, such as MATLAB, and Free and Open Source Software (FOSS) computational environments, such as Octave and Python, for performing signal processing, analysis, and algorithm development. Future developments include extending the Web interface into a portal system for accessing ARL algorithms and signatures, High Performance Computing (HPC) resources, and integrating existing database and signature architectures into sensor networking environments.