The use of sensors for undersea warfare (USW) missions involves a wide spectrum of approaches. Among the many related research and development thrusts is a healthy focus on computer automation and flexible sensor positioning by low cost semi-autonomous platforms. Automation is being applied to organic sensors on large capital ships as well as distributed sensors detached from a central command center for the purposes of increasing area search rate, managing workload, and reducing cost. Particular capabilities are driven by mission-specific considerations such as large area or barrier search in deep water, shallow littorals, or riverine locations. For example, automation incorporated in undersea sensors on mobile unmanned vehicles will likely be different than approaches implemented on larger ships. Likewise, leave behind sensors on the surface or bottom will provide yet different performance attributes. Autonomous platforms including the unmanned undersea vehicle (UUV), unmanned surface vehicle (USV) and unmanned air vehicles (UAV) will host sensors that play a key role. The resulting landscape includes a fairly intricate set of sensor types, platforms, and operational methods. An overview of selected unmanned and/or unattended naval undersea sensor technologies is discussed, along with some of the inherent capabilities that make them advantageous to specific USW missions. One example of cost savings achievable through extensive use of automation is provided to illustrate potential ancillary benefits. The primary technical challenges that need to be overcome before these sensors can reach their desired operational capability are also discussed.

Acoustic sensors mounted to a tethered aerostat detect and localize transient signals from mortars, artillery, C-4, propane cannon, and small arms fire. Significant enhancements to soldier lethality and survivability can be gained when using the aerostat array to detect, localize, and cue an aerial imager to a weapon's launch site, or use the aerostat's instantaneous position and orientation to calculate a vector solution to the ground coordinates of the launch site for threat neutralization. The prototype aerostat-mounted array was tested at Yuma Proving Grounds (YPG) as part of the NATO TG-53 signature collection exercise. Acoustic wave form data was collected simultaneously with aerostat and ground-based sensor arrays for comparing wind noise, signal to noise related parameters, and atmospheric effects on propagation to an elevated array. A test description and summary of localization accuracy will be presented for various altitudes, ranges to target, and under differing meteorological conditions.

The Acoustics Signal Processing Branch at the U.S. Army Research Laboratory has been investigating tracking and classification of military and civilian vehicles using acoustic sensors. Currently the target signals are modeled as a sum of harmonics and then they are classified using multivariate Gaussian classifier at each individual node. When multiple targets are present in the scene overall classification of the targets deteriorates as the signals from several targets are mixed together and determinations of individual target harmonics become difficult. This is true particularly for civilian vehicles. In order to improve the overall probability of correct classification a distributed classifier will be implemented. In a distributed processing each sensor node would broadcast the classification information, that is, probability of detection of various targets, to all the sensors within its vicinity. At each sensor node a distributed Bayesian classifier is used to determine the overall classification of each target. The distributed processing is robust to failures in sensor nodes unlike the centralized processing. Although the technique is known, it had been tested using only simulated data. In this paper we present the results of the algorithm on real data that was collected using several acoustic sensors using a mixture of military and civilian vehicles. This would identify how well the distributed processing works or its limitations in classifying multiple targets using acoustic data.

In response to the needs of the UK MOD QinetiQ have designed, developed and trialled an ad-hoc, self organising network of acoustic nodes for in-depth deployment that can detect and track military targets in a range of environments and for all types of weapon locating. Research conducted has shown that disposable technologies are sufficiently mature to provide a useful military capability. Work this year has included a 3 month series of trials to exercise the prototype equipment and has provided an indication of in-service capability across a broad range of environments. This paper will discuss the scientific approach that was applied to the development of the equipment, from early laboratory development through to the prototype sensor network deployment in operationally representative environments. Highlights from the trials have been provided. New findings from the fusion of a low cost thermal imager that can be cued by the acoustic network are also discussed.

The problem of detection and localization of multiple acoustic sources using unattended passive sensors is considered. Existing wideband Capon direction of arrival (DOA) estimation methods typically fail to accurately detect and resolve multiple closely spaced sources in presence of model mismatches and wavefront perturbations caused by senor location errors and near-field effects. This paper applies a set of adaptive beamforming methods in reduced dimension subspace for non-ideal acoustic array sensing scenarios. A robust wideband Capon method is studied to account for the inherent uncertainties in the array steering vector. To improve the resolution within a sector of interest the beamspace method is extended and applied to this problem. These methods are then implemented and benchmarked on real acoustic signatures of multiple ground vehicles moving in tight formations.

General Sensing Systems (GSS) has achieved outstanding and verifiable results in the design and performance of seismic systems with near zero false alarm rates for the detection of walking, running, and jumping persons. These results have been reported in various homeland security and military applications. GSS has been developing a new seismic, unattended small size module for footstep detection. This paper describes our design for the module, which includes the two-board and one-board versions - fitting into various consumer applications. Communication interface versions, which are used in the detection module, allow the communication with any wireless surveillance network. We also report on the preliminary lab and field-testing that was implemented in various conditions. We show that the new unattended, small size detection module demonstrates the same reliable performance as our previous full size systems.

Field studies were conducted in 2005 in Yuma, Arizona at the Yuma Proving Grounds (YPG) to document seismic signatures of walking humans. Walker-generated vertical ground vibrations were recorded using standard omni-directional 4.5 Hz peak-resonance geophones. Walker position and speed were measured using portable GPS equipment. Collected seismic data were processed and hypothetical sensor performance predictions were made using an algorithm developed for the detection and classification of a walking intruder. Sample results for the Yuma study are presented in the form of sensor detection/classification vs. range plots, and color-coded animations of seismic sensor alarm annunciations during walking intruder tests. A perimeter intrusion scenario for a Forward Operating Base is defined that involves a walker approaching a sensor picket-line along a path exactly halfway between two adjacent sensors. This is considered a conservative representation of the perimeter intrusion problem. Summary plots derived from a binomial probability based analysis define intruder detection probabilities for different sensor spacings. For a 215 lb intruder walking in the Yuma test environment, a 90% probability of at least two walker-classified sensor detections is achieved at a sensor spacing of 140 m. Preliminary investigations show the intruder classification component of the discussed detection/classification algorithm to perform well at rejecting signals associated with a nearby idling vehicle and normal background noise.

The advancement of neural network methods and technologies is finding applications in many fields and disciplines of interest to the defense, intelligence, and homeland security communities. Rapidly reconfigurable sensors for real or near-real time signal or image processing can be used for multi-functional purposes such as image compression, target tracking, image fusion, edge detection, thresholding, pattern recognition, and atmospheric turbulence compensation to name a few. A neural network based smart sensor is described that can accomplish these tasks individually or in combination, in real-time or near real-time. As a computationally intensive example, the case of optical imaging through volume turbulence is addressed. For imaging systems in the visible and near infrared part of the electromagnetic spectrum, the atmosphere is often the dominant factor in reducing the imaging system's resolution and image quality. The neural network approach described in this paper is shown to present a viable means for implementing turbulence compensation techniques for near-field and distributed turbulence scenarios. Representative high-speed neural network hardware is presented. Existing 2-D cellular neural network (CNN) hardware is capable of 3 trillion operations per second with peta-operations per second possible using current 3-D manufacturing processes. This hardware can be used for high-speed applications that require fast convolutions and de-convolutions. Existing 3-D artificial neural network technology is capable of peta-operations per second and can be used for fast array processing operations. Methods for optical imaging through distributed turbulence are discussed, simulation results are presented and computational and performance assessments are provided.

This paper describes recent advances in the technology for, and implementation of, short-range non-line-of-sight (NLOS) optical communication links. The approach relies on molecular scattering of ultraviolet wavelengths by the atmosphere to achieve NLOS, omni-directional communication Links. The implementation employs commercially produced semiconductor sources emitting in the solar-blind region of the UV spectrum, around 275nm. This paper extends previously reported field measurements to longer ranges (100+m) and to a wider variety of application scenarios, including an outdoor demonstration of real-time speech at 2.4kbps in full sunlight. The paper also addresses the design trades associated with replacing photomultiplier detectors with semiconductor detectors for reasons of cost and ruggedness. Even with improvements in semiconductor materials and commensurate reduction in dark currents, the use of semiconductor detectors will require the introduction of imaging arrays. Incorporation of imaging arrays opens the possibility of adaptive links in which bandwidth and transmit power are adapted to best exploit the channel constraints.

Research currently underway at Georgia Tech focuses on various sensor modalities for detecting covert personnel. One component of that research examines visible band signatures as a means of detection. In particular, concepts centered on non-imaging and low pixel count sensors have been investigated. This paper presents results from that study with particular emphasis on non-imaging spectral sensor concepts. Results from analysis, modeling, and measurements will be presented. Additional concepts for low pixel count sensors will also be presented. Environmental effects including local lighting conditions have been incorporated into the analyses to derive sensor requirements.

The authors have invented and are developing a new Real-Time Stereoscopic Catadioptric Omni-Detection (RSCO) system based on a high-resolution stereoscopic vision system, an acoustic array with high-fidelity/sensitivity, real-time image processing hardware based on evolutionary stream processing hardware architecture based on the processors (SPs) with unprecedented digital signal processing (DSP) performance, and proprietary unwrapping and operating software. The RSCO development involves optimizing (1) the omnidirectional multimodal (visible/MWIR) sensor system configuration and mechanical hardening to survive harsh conditions and to minimize maintenance; (2) a video/image ultrahigh-performance SPs, (3) an acoustic array with high-performance DSP technology; and (4) real-time unwrapping and operating software. SPs form a new class of image processors scalable to teraOPS, with efficiency comparable to ASICs, and completely programmable in high-level languages. SPs innovatively combine a new programming model, tool automation, and hardware to exploit the high data parallelism and processing locality that are inherent in a wide range of applications, especially media processing. The excellent performance, efficiency, and programmability of SPs make them ideal for implementation of the RSCO system, with its unprecedented omnidirectional, multiple-modality sensors, range, accuracy, size, and robustness.

Ultra-wide band (UWB) ground wave (GW) is a novel means of communications for use with distributed networked sensors at sea. Although multiple distributed sensor systems are in development, the communications method for these systems has yet to be fully realized. The buoys that relay the sensor information have several key features: they must be small enough so they are not highly noticeable and do not pose a navigation hazard; they must be cheap enough to be expendable; they must be able to run on limited battery power; the communications link must be at a great enough distance so that fewer buoys are needed; and they must deal with multipath from the sea surface. Ultra-wide band ground wave will address many of these issues. UWB is being developed commercially at 3-10 GHz. UWB requires low power and the transmitters are extremely easy to implement making the system inexpensive and small. UWB provides low probability of detection and interception. However commercial UWB operates at very short distances. Implementing UWB Ground Wave instead of commercial-band UWB will extend the communication range between buoys up to 10 miles. The distributed sensors will transmit to a central buoy for data relay via satellite or communicate directly to a submarine, UUV or surface ship antenna. This project is currently being funded by Office of Naval Research (ONR) 313. The project commenced in October 2005.

Detection, classification, and localization of potential security breaches in extremely high-noise environments are important for perimeter protection and threat detection both for homeland security and for military force protection. Physical Optics Corporation has developed a threat detection system to separate acoustic signatures from unknown, mixed sources embedded in extremely high-noise environments where signal-to-noise ratios (SNRs) are very low. Associated neural network structures based on independent component analysis are designed to detect/separate new acoustic sources and to provide reliability information. The structures are tested through computer simulations for each critical component, including a spontaneous detection algorithm for potential threat detection without a predefined knowledge base, a fast target separation algorithm, and nonparametric methodology for quantified confidence measure. The results show that the method discussed can separate hidden acoustic sources of SNR in 5 dB noisy environments with an accuracy of 80%.

Several advanced algorithms in defense and security objectives require high-speed computation of nonlinear functions. These include detection, localization, and identification. Increasingly, such computations must be performed in double precision accuracy in real time. In this paper, we develop a significance-based interpolative approach to such evaluations for double precision arguments. It is shown that our approach requires only one major multiplication, which leads to a unified and fast, two-cycle, VLSI architecture for mantissa computations. In contrast, the traditional iterative computations require several cycles to converge and typically these computations vary a lot from one function to another. Moreover, when the evaluation pertains to a compound or concatenated function, the overall time required becomes the sum of the times required by the individual operations. For our approach, the time required remains two cycles even for such compound or concatenated functions. Very importantly, the paper develops a key formula for predicting and bounding the worst case arithmetic error. This new result enables the designer to quickly select the architectural parameters without the expensive and intolerably long simulations, while guaranteeing the desired accuracy. The specific application focus is the mapping of the Independent Component Analysis (ICA) technique to a coarse-grain parallel-processing architecture.

Wireless sensors provide solutions to otherwise intractable problems in homeland defense and security, building automation, industrial process monitoring and control, structural health monitoring in bridges, aircraft, buildings and ships and a host of applications where the cost or feasibility of deploying wired sensors is impractical. The biggest challenge facing wireless sensors is power. While advances in power management and battery technology may enable 3 to 5 year battery life, in many applications even this is not sufficient. Energy harvesting techniques show potential to provide long lasting power, but suffer from low power density values, meaning that they must be oversized to deliver power for short duty cycle communications functions. In this paper we demonstrate a solution that draws on the benefits of energy harvesting for long life, and microbatteries and microsupercapacitors to provide back-up and pulse power capabilities without the need for refueling or recharging.

The Unattended Ground Sensor (UGS) Community includes many programs slated to incorporate the JTRS Cluster 5 Transceiver into the baseline systems. However, due to a variety of technical challenges, it's unclear that the Cluster 5 Transceiver will be available in time to support requirements for near-term fieldings. As a result, program offices are beginning to investigate interim solutions for Software Defined Radios (SDR) capable of providing Cluster 5-like flexibility and performance. During the last 15 months, Nova Engineering has been developing the Core Transceiver, a unique multi-band, multi-rate, Software Defined Radio. The Core Transceiver operates in four bands (225-400 MHz, 560-698 MHz, 1350-1390 MHz, and 1755-1850 MHz) and is frequency-agile across these multiple bands, thereby providing maximum flexibility in frequency planning. The transceiver supports adaptable data rates from 100 kbps to more than 1 Mbps, enabling the device to satisfy requirements for sensor-to-sensor communications, as well as gateway links. This transceiver utilizes an Orthogonal Frequency Division Multiplex (OFDM) waveform for robust operation in ground-to-ground multipath environments. The Core Transceiver is compact in size (6.5" x 2.85" x 0.8") and designed for energy-constrained applications. This paper introduces the Core Transceiver requirements, design concept, and implementation, and outlines a strategy for applying this transceiver within the UGS Community.

The promise of acoustic classification of vehicles is based on the expectations provided by the human ability to differentiate sounds from familiar vehicles. Some of these promises have not been fully achieved in practice, necessitating a "reality check" on the uses and limitations of the technology. Some of those limitations are: Operation in the real-world environment of outdoor propagation over rough terrain in the presence of natural and cultural background noise sources. A great number of the new applications are used against civilian-type vehicles that have emissions that are substantially lower than those of military vehicles. The success of speech recognition systems has also fueled some of these expectations. But before we take the analogy too far, we must note that there is a vast difference between the two tasks. Speech occupies a wider bandwidth than vehicle noise and is much more richly modulated than vehicle noises. Consequently there is much more information content to extract and more features to rely on than in the vehicle classification problem. Starting with a vehicle classification algorithm based on a neural network trained to recognize two different vehicles, we illustrate how by creating a continuous track on the target and integrating the output of the classifier over the life of the track we can improve the confidence of the classification results. Similarly fusing the results obtained by two or more sensors spread around the target can further improve the classification performance, cutting the rate of erroneous classifications by a third or more.

Acoustic vector sensors measure the acoustic pressure and three orthogonal components of the acoustic particle acceleration at a single point in space. These sensors, and arrays composed of them, have a number of advantages over traditional hydrophone arrays. This includes full azimuth/elevation angle estimation, even with a single sensor. It is of interest to see how in-water vector sensor performance matches theoretical bounds. A series of experiments designed to characterize the performance of vector sensors operating in shallow water was conducted to assess sensor mounting techniques, and evaluate the sensor's ability to measure bearing and elevation angles to a source as a function of waveform characteristics and signal-to-noise ratio.

A transceiver has been designed for an Unattended Ground Sensor (UGS) system which meets several key requirements that are shared by numerous sensor networks. The communication subsystem can be configured quickly through the use of secure, low-power RF links. An efficient media access layer provides low latencies during high traffic scenarios. A robust networking algorithm allows the network to adapt to changes in the RF environment, including remote jamming. A low power consumption hardware design allows each unit to achieve long mission durations while operating off of limited battery resources. A highly efficient RF front end allows for several kilometer link ranges to be achieved with ground level antennas. The transceiver hardware conforms to a small form factor designed for high volume production with low per-unit cost. While the transceiver was specifically designed for a particular system, the hardware platform can easily be configured to suit a variety of sensor applications. Because waveform modulation and demodulation are executed via digital signal processing, changes to modulation technique and data rate can be accommodated. The DSP and PLD based digital architecture provides a software definable radio platform that serves as a mature and tested alternative to the developmental JTRS Cluster 5 radio.

This paper presents a novel self-initializing algorithm using Change Detection to achieve self-awareness of unusual conditions without a prior modeling assumptions. Deviations from baseline nominal conditions yield a tip-off and the variation off baseline indicates a novelty to be logged for publication. Incremental processing of the data log enables common transients to be ignored and viewed as nominal. In this framework, only second pass novelties invoke enough interest for publication. The mathematical methods for enabling this exploit both classical control theory transfer functions to model the environment and O(3ⁿ) Volterra series type polynomials as an innovative change detection method without explicit modeling.

Microbes, like Geobacters, have inhabited the seafloors around the world since the early days of earth. Such regions are anaerobic and they gain energy by using the widely prevalent iron oxides and organic matters. Because they appear to colonize conducting surfaces that act as sinks of electrons, microbial fuel cells have been shown to convert organic matter to electricity. A microbial fuel cell system has been deployed in Narragansett Bay in Newport, Rhode Island for a year. Currently, the cathode and anode areas are of the order of that of a small wind mill. Measurements have been carried out to determine the marine scaling laws of power harvesting in passive benthic microbial fuel cells. The focus has been on the ocean engineering aspects such as marine scaling laws and the integration of the biochemical and the electronic systems. The characteristics examined are: the relationship of electrode surface area and power produced, the stabilization rates of ionic paths, that is, the effects of location depth of cathodes on stabilization after deployment, the effects of solar and lunar cycles in the Narragansett Bay on the dynamic components of power produced, and the hysteresis effects between periods of active power harvesting and dormancy; the effects of 'on sediment surface' versus 'in sediment' anode deployment have been examined for smaller electrode areas so far. A capacitance model of power consumption and harvesting has been proposed for the marine environment. It is assumed that the primordial benthic microbe laden layer of the earth acts like a giant capacitor. In the microbial fuel cell, this charged benthic layer acts in series with a smaller constant voltage DC power source. This giant benthic capacitance is a result of untapped accumulated charge from the microbes while the DC source originates from the real-time production due to the microbes. Finally, the microbial fuel cell is integrated with a power conversion system to intermittently energize a small incandescent lantern in the NUWC Stillwater Basin located in Narragansett Bay in Rhode Island.

One of the significant problems in visual tracking of objects is the requirement for a human analyst to post-process and interpret the data. For instance, consider the task of tracking a target, in this case a moving person, using video imagery. When this person hides behind an obstruction, and is therefore no longer visible by the camera, conventional tracking systems quickly lose track of the target and are no longer able to indicate where the target is or where it was headed. A human interpreter is then needed to conclude that the person is hiding, and probably (with certain probability) is still there. A Process Query System (PQS) is able to track and predict the path of arbitrary objects, based only on a description of their dynamic behavior, thus eliminating the need for precise identification of each object in every frame. The PQS is therefore able to draw human-like conclusions, allowing the system to track the person even when he/she is out of view. Additionally, using dynamic descriptions of tracked objects allows for low-quality video signals, or even infrared video, to be used for tracking. In this paper we introduce a novel way of implementing a video-based tracking system using a Process Query System to predict the position of objects in the environment, even after they have disappeared from view. Although the image processing pipeline is trivial, tracking accuracy is remarkably high, suggesting that overall performance can be improved even further with the use of more sophisticated video processing and image recognition technology.

The concept of trackability is intimately related to the establishment of optimal trade-offs between the nosiness of the environment, due to poor sensing, and the randomness of the kinematics of the phenomena being examined, due to poor knowledge of their behaviors. Classically, a sensor system receives low level data in the form of numerical or analog signals and then through signal processing produces a high level observation suitable for a higher level state estimation process. These two phases may be further refined into a hierarchical chain of "tiers", where observations at each level are obtained through the computation of a set of properties of the system's estimated state at the lower level. An important factor that seems to have an impact on the overall ability to track high level phenomena in real time is the computational complexity of deciding those properties when generating observations between the tiers. And this complexity characterizes the accuracy of what can be computed within a bounded time frame. In this paper we intend to investigate the "real time" trackability of phenomena through the analysis of the complexity of individual models in relation to the computational complexity of computing observations in any multi-tiered tracking system.

In this paper, we discuss the NATO Task Group 53 (TG-53) acoustic detection of weapon firing field joint experiment at Yuma Proving Ground during 31 October to 4 November 2005. The participating NATO countries include France, the Netherlands, UK and US. The objectives of the joint experiments are: (i) to collect acoustic signatures of direct and indirect firings from weapons such as sniper, mortar, artillery and C4 explosives and (ii) to share signatures among NATO partners from a variety of acoustic sensing platforms on the ground and in the air distributed over a wide area.

The U.S. Army Corps of Engineers Engineer Research and Development Center (ERDC) participated in a joint ARL-NATO TG-53 field experiment and data collection at Yuma Proving Ground, AZ, in early November 2005. Seismic and acoustic signatures from both muzzle blasts and impacts of small arms fire and artillery were recorded using seven seismic arrays and three acoustic arrays. Arrays composed of 12 seismic and 12 acoustic sensors each were located from 700 m to 18 km from gun positions. Preliminary analysis of signatures attributed to 60-mm, 81-mm, and 120-mm mortars recorded at a seismic-acoustic array 1.1 km from gun position are presented. Seismic and acoustic array f-k analysis is performed to detect and characterize the source signature. Horizontal seismic data are analyzed to determine efficacy of a seismic discriminant for mortar and artillery sources. Rotation of North and East seismic components to radial and transverse components relative to the source-receiver path provide maximum surface wave amplitude on the transverse component. Angles of rotation agree well with frequency-wavenumber (f-k) analysis of both seismic and acoustic signals. The spectral energy of the rotated transverse surface wave is observable on all caliber of mortars at a distance of 1.1 km and is a reliable source discriminant for mortar sources at this distance.

Passive ranging techniques are used in land-based acoustic surveillance systems and underwater sonar systems to localize sources that radiate acoustic energy into the environment. Passive ranging by wavefront curvature relies on the spherical expansion of the wavefronts as the acoustic energy propagates outwards from the source. A wide-aperture receiving array is used to sense the curvature of the wavefront by estimating the intersensor time delays as the wavefront traverses the array. The time delay estimates are used to calculate the range (which is equal to the radius of curvature of the wavefront) and bearing of the source. The wavefront curvature method is applied here to the passive ranging of sources of four different types of acoustic signals: underwater mechanical transients, underwater biological transients, continuous sound wave transmissions in air and impulsive sounds in air. The method provides precise range and bearing estimates of underwater signal sources. In comparison, large passive ranging errors are observed for in-air sources because the atmosphere is a nonstationary sound propagation medium. Atmospheric turbulence causes perturbations in the curvature of the acoustic wavefronts and leads to random fluctuations in the source position estimates on time scales ranging from seconds to minutes. Background noise at each sensor has only a small effect on the positional uncertainty of in-air sources with random fluctuations in the source position estimates occurring on subsecond time scales.

The detection and localization of artillery guns on the battlefield is envisaged by means of acoustic aerial waves including sonic and infrasonic waves. The main objective of this work is to examine the different frequency ranges usable for the detection of small arms, mortars and artillery guns on the same hardware platform. The main stages of this study have consisted of: 1. data acquisition of the acoustic signals of artillery guns and mortars, 2. modeling of the wave propagation in the atmosphere, 3. signal processing and evaluation of the localization performance for an individual array, and 4. an absolute X,Y localization based on a network of sensors will be studied at a later stage.

Ferret is an acoustic system that detects, recognizes and localizes the source and direction of small arms fire. The system comprises a small array of microphones and pressure sensors connected to a standard PC-104 computer that analyzes, displays, reports and logs the parameters of a recognized shot. The system operates by detecting and recognizing the ballistic shock waves created by the supersonic bullet, combined with the muzzle blast wave propagating from the weapon. The system was recently installed and tested on a patrol boat operated by the Royal Canadian Mounted Police (RCMP). An electronic compass with tilt compensation and a GPS was incorporated into the system. This allows the system to correct for the motion of the boat and provide the full coordinates of the shooter. The system also updates the azimuth to the shooter in real time as the boat turns. This paper presents the results of our test and evaluation based on a live firing experiment. Ferret is the result of a collaborative effort by Defence R&D Canada and MacDonald Dettwiler and Associates.

McQ has developed a family of low cost unattended ground sensors using conventional technology and manufacturing techniques. Intended for small unit operations in an urban environment, these are tactically useful sensors that can be manufactured in large quantities (1M-10M units/year) for a projected production cost of less than $100 each. Secondary characteristics are small size (98 cm³), light weight (85 gm), moderate lifetime (40 hrs), and moderate communications ranges (100m). An overview of the DSS system: its features, performance, and scenarios for use in urban warfare, is presented.

This paper presents a framework and demonstrates results from a process detection based approach to tracking an airborne plume in sensor networks. Data integration and pattern detection in large sensor networks measuring gas and radiation plumes suffer from low resolution observations, missed detections, and numerous false positive reports. Large numbers of nodes and the hypothesis management concept of a Process Query System (PQS) can compensate for lower data quality. A result of the process detection based approach to this problem is models that can be implemented in many different scenarios. Plume predictor models are illustrated which allow data association between sensor nodes in typical outdoor wind conditions. We demonstrate a simulation of a mobile plume source in a sensor network designed for use in the same PQS. A kinematic model is developed for a vehicle carrying a plume source. Inverse models for this mobile plume source will work in conjunction with the existing software systems, thus allowing PQS to rapidly be adapted to a new problem domain with minimal modifications. This scenario of a mobile airborne plume source approximates a moving container emitting a detectable substance in a transportation network, where the container movement is restricted by existing vehicle corridors.

Modern Unattended Ground Sensor (UGS) systems require transmission of high quality imagery to a remote location while meeting severe operational constraints such as extended mission life using battery operation. This paper describes a robust imagery system that provides excellent performance for both long range and short range stand-off scenarios. The imaging devices include a joint EO and IR solution that features low power consumption, quick turn-on time, high resolution images, advanced AGC and exposure control algorithms, digital zoom, and compact packaging. Intelligent camera operation is provided by the System Controller, which allows fusion of multiple sensor inputs and intelligent target recognition. The System Controller's communications package is interoperable with all SEIWG-005 compliant sensors. Image transmission is provided via VHF, UHF, or SATCOM links. The system has undergone testing at Yuma Proving Ground and Ft. Huachuca, as well as extensive company testing. Results from these field tests are given.

The U.S. Army Research Laboratory has developed a real-time multi-modal sensor for the purpose of personnel detection in urban terrain. Possible system usage includes force protection and sniper early warning. The sensor system includes a network of MMUGS sensors, a third-party gateway and user interface device. A MMUGS sensor consists of the following functions: sensing, processing, and communication. Each sensor is composed of multiple sensing modalities-acoustic, passive-infrared, and seismic. A MMUGS sensor is designed to be low cost and power efficient. This paper will first present an overview of the sensor architecture and then provide detailed descriptions of sub components. The paper will conclude with a detailed analysis of system performance. This paper is intended to provide details of the design, integration, and implementation of a MMUGS unit, and demonstrate the overall sensor system performance. This paper does not discuss the network aspect of the system and its affect on performance.

McQ has produced a family of small (98 cm³), inexpensive ($100), unattended ground sensors well suited for urban environments. As a result, a broad range of data has been collected in urban settings. This paper discusses human signatures in urban environments using low cost seismic, infrared, acoustic, and magnetic transducers. Transducer performance and the effects of orientation, building construction, and environmental noise will be focused on. Detection methods used to exploit signatures and resulting performance statistics will also be discussed.

This paper summarizes the Defense Threat Reduction Agency (DTRA) sponsored development and demonstration of an Air Launched Sensor Node (ALSN) system designed to fill DTRA's immediate need to support the Global Strike requirement of weapon-borne deliverable sensors for Battle Damage Assessment (BDA). Unattended ground sensors were integrated into a CBU-103 Tactical Munitions Dispenser (TMD), and flight test demonstrated with the 46^th Test Wing at Eglin AFB, FL. The objectives of the ALSN program were to repackage an existing multi-sensor node system to conform to the payload envelope and deployment configuration design; to integrate this payload into the CBU-103 TMD; and to conduct a combined payload flight test demonstration. The final sensor node included multiple sensors a microphone, a geophone, and multiple directional Passive Infrared (PIR) detectors with processing electronics, a low power wireless communications 802.15.4 mesh network, GPS (Global Positioning System), and power integrated into a form-fit BLU-97 munitions deployable package. This paper will present and discuss the flight test, results, and ALSN performance.

Unattended sensor systems are new technologies that are supposed to provide enhanced situation awareness to military and law enforcement agencies. A network of such sensors cannot be very effective in field conditions only if it can transmit visual information to human operators or alert them on motion. In the real field conditions, events may happen in many nodes of a network simultaneously. But the real number of control personnel is always limited, and attention of human operators can be simply attracted to particular network nodes, while more dangerous threat may be unnoticed at the same time in the other nodes. Sensor networks would be more effective if equipped with a system that is similar to human vision in its abilities to understand visual information. Human vision uses for that a rough but wide peripheral system that tracks motions and regions of interests, narrow but precise foveal vision that analyzes and recognizes objects in the center of selected region of interest, and visual intelligence that provides scene and object contexts and resolves ambiguity and uncertainty in the visual information. Biologically-inspired Network-Symbolic models convert image information into an 'understandable' Network-Symbolic format, which is similar to relational knowledge models. The equivalent of interaction between peripheral and foveal systems in the network-symbolic system is achieved via interaction between Visual and Object Buffers and the top-level knowledge system.

The US Navy has recently placed emphasis on deployable, distributed sensors for Force Protection, Anti-Terrorism and Homeland Defense missions. The Naval Undersea Warfare Center has embarked on the development of a self-contained deployable node that is composed of electro-active polymers (EAP) for use in a covert persistent distributed surveillance system. Electro-Active Polymers (EAP) have matured to a level that permits their application in energy harvesting, hydrophones, electro-elastic actuation and electroluminescence. The problem to resolve is combining each of these functions into an autonomous sensing platform. The concept presented here promises an operational life several orders of magnitude beyond what is expected of a Sonobuoy due to energy conservation and harvesting, and at a reasonable cost. The embodiment envisioned is that of a deployed device resembling a jellyfish, made in most part of polymers, with the body encapsulating the necessary electronic processing and communications package and the tentacles of the jellyfish housing the sonar sensors. It will be small, neutrally buoyant, and will survey the water column much in the manner of a Cartesian Diver. By using the Electro-Active Polymers as artificial muscles, the motion of the jellyfish can be finely controlled. An increased range of detection and true node autonomy is achieved through volumetric array beamforming to focus the direction of interrogation and to null-out extraneous ambient noise.

This paper describes the use of unattended seismic and acoustic Unattended Ground Sensors (UGS) for aircraft detection and activity categorization. Such systems were tested for these purposes and found to be particularly effective. The multi-modal sensor system discussed here was tested in September of 2005 and again in January of 2006. Test results are detailed and conclusions about the way ahead are drawn to facilitate further research.