This PDF file contains the front matter associated with SPIE Proceedings Volume 8046, including the Title Page, Copyright information, Table of Contents, and Conference Committee listing.

The presence of snipers in modern conflicts leads to high insecurity for the soldiers. In order to improve the soldier's protection against this threat, the French German Research Institute of Saint-Louis (ISL) initiated studies in the domain of acoustic localization of shots. Mobile antennas mounted on the soldier's helmet were initially used for real-time detection, classification and localization of sniper shots. It showed good performances in land scenarios, but also in urban scenarios if the array was in the shot corridor, meaning that the microphones first detect the direct wave and then the reflections of the Mach and muzzle waves. As soon as the acoustic arrays were not near to the shot corridor (only reflections are detected) this solution lost its efficiency and erroneous estimated position were given. In order to estimate the position of the shooter in every kind of urban scenario, ISL started studying time reversal techniques. Knowing the position of every reflective object in the environment (buildings, walls, ...) it should be possible to estimate the position of the shooter. First, a synthetic propagation algorithm has been developed and validated for real scale applications. It has then been validated for small scale models, allowing us to test our time reversal based algorithms in our laboratory. In this paper we discuss all the challenges that are induced by the application of sniper detection using time reversal techniques. We will discuss all the hard points that can be encountered and try to find some solutions in order to optimize the use of this technique.

In a recent paper [P. Rizzo, G. Bordoni, A. Marzani, and J. Vipperman, "Localization of Sound Sources by Means of Unidirectional Microphones, Meas. Sci. Tech., 20, 055202 (12pp), 2009] the proof-of-concept of an approach for the localization of acoustic sources was presented. The method relies on the use of unidirectional microphones and amplitude-based signals' features to extract information about the direction of the incoming sound. By intersecting the directions identified by a pair of microphones, the position of the emitting source can be identified. In this paper we expand the work presented previously by assessing the effectiveness of the approach for the localization of an acoustic source in an indoor setting. As the method relies on the accurate knowledge of the microphones directivity, analytical expression of the acoustic sensors polar pattern were derived by testing them in an anechoic chamber. Then an experiment was conducted in an empty laboratory by using an array of three unidirectional microphones. The ability to locate the position of a commercial speaker placed at different positions in the room is discussed. The objective of this study is to propose a valid alternative to the common application of spaced arrays and therefore to introduce a new generation of reduced size sound detectors and localizers. The ability of the proposed methodology to locate the position of a commercial speaker placed at different positions in the room was evaluated and compared to the accuracy provided by a conventional time delay estimate algorithm.

In this work we consider the localization of a gunshot using a distributed sensor network measuring time differences of arrival between a firearm's muzzle blast and the shockwave induced by a supersonic bullet. This so-called MB-SW approach is desirable because time synchronization is not required between the sensors, however it suffers from increased computational complexity and requires knowledge of the bullet's velocity at all points along its trajectory. While the actual velocity profile of a particular gunshot is unknown, one may use a parameterized model for the velocity profile and simultaneously fit the model and localize the shooter. In this paper we study efficient solutions for the localization problem and identify deceleration models that trade off localization accuracy and computational complexity. We also develop a statistical analysis that includes bias due to mismatch between the true and actual deceleration models and covariance due to additive noise.

We report a compact, portable, low power tunable diode laser based sensor for a fast, non-intrusive measurement of temperature on airborne-based vehicles. The proposed sensor design avoids common problems in existing sensors such as adiabatic compression of the ambient airstream, thermal inertia of the sensing element, and impinging cloud particles. These effects are quite common in the conventional temperature sensors used in most aerial vehicles for ambient temperature measurements. The molecular oxygen transitions are measured using a 765 nm wavelength range vertical cavity surface emitting laser in the spectral region of two closely spaced oxygen transitions, centered at 13069.95 cm^-1and 13068.07 cm^-1 respectively, according to HITRAN database. Another advantage of the proposed sensor design is that it can simultaneously detect additional trace gas species along with in-situ temperature measurements. For example, in this design we detect carbon dioxide concentration using a 2000 nm wavelength laser. The two laser beams are co-aligned and coupled into a single 20 cm multipass cell. The absorption signal (from both carbon-dioxide and oxygen) was detected simultaneously on a 2 micron photodetector. Second harmonic (Nf, N=2) detection, using wavelength modulation spectroscopy was employed to enhance the sensitivity of measurements. The sensor can readily be miniaturized and consumes less than 2 W of power, ideal for use of unmanned aerial systems and other airborne platforms.

Algorithms for synergistically fusing acoustic and optical sensory inputs, thereby mimicking biological attentional processes are described. Manual existing perimeter defense surveillance systems using more than one sensory modality combine different sensors' information to corroborate findings by other sensors and to add data from a second modality. In contrast to how conventional systems work, animals use information from multiple sensory inputs in a way that improves each sensory system's performance. We demonstrated that performance is enhanced when information in one modality is used to focus processing in the other modality (a form of attention). This synergistic bi-modal operation improves surveillance efficacy by focusing auditory and visual "attention" on a particular target or location. Algorithms for focusing auditory and visual sensors using detection information were developed. These combination algorithms perform "zoom-with-enhanced-acuity" in both the visual and auditory domains, triggered by detection in either domain. Sensory-input processing algorithms focus on specific locations, indicated by at least one of the modalities. This spatially focused processing emulates biological attention-driven focusing. We showed that given information about the target, the acoustic algorithms were able to achieve over 80% correct target detection at signal-tonoise ratios (SNRs) of -20 dB and above, as compared with similar performance at SNRs of -10 db and above without target information from another modality. Similarly, the visual algorithm achieved performance of over 80% detection with added noise variance of 0.001 without target indication, but maintained 100% detection at added noise variance of 0.05 when acoustic target information was taken into account.

This paper highlights the most recently added features and benefits available in the latest generation of Northrop Grumman SCORPION II persistent surveillance and target recognition systems. By leveraging smaller, lighter, and more power efficient SCORPION II sensor and universal gateway components, with foliage penetrating ad-hoc network communications, persistent field programmable systems that are easier to conceal can be optimized for both image capture and data exfiltration. In addition to the SCORPION II suite of sensor components, a growing list of over sixty different sensor and camera types from a variety of manufacturers have been integrated with the SCORPION Gateway family. In addition to updating several different COP systems, SCORPION and SCORPION II data can be directly processed using a common sensor status graphical user interface (GUI) that allows for viewing and analysis of images and sensor data from hundreds of SCORPION system gateways on single or multiple displays.

A comprehensive Modeling and Simulation (M&S) capability is required to assess, improve and visualize newly fielded technologies for Critical Asset Protection (CAP). Protection is addressed in the planning stages of any secure facility in order to provide adequate defense of critical personnel and assets. Given the increased threats domestically and globally, the security missions are dynamic and technically challenging. Operational forces need to be proactive and leave no gaps within the complex, integrated and layered defensive posture. This paper will discuss a research and development effort to use Government off the Shelf Software (GOTS) to complete this task; by linking high resolution simulation / training software and an operational simulation analysis software in a distributed environment.

This paper provides a statistical analysis method for detecting and discriminating different seismic activity sources such as humans, animals, and vehicles using their seismic signals. A five-step process is employed for this purpose: (1) a set of signals with known seismic activities are utilized to verify the algorithms; (2) for each data file, the vibration signal is segmented by a sliding-window and its noise is reduced; (3) a set of features is extracted from each window of the signal which captures its statistical and spectral properties. This set is formed as an array and is called a feature array; (4) a portion of the labeled feature arrays are utilized to train a classifier for discriminating different types of signals; and (5) the rest of the labeled feature arrays are employed to test the performance of the developed classifier. The results indicate that the classifier achieves probability of detection (p_d) above 95% and false alarm rate (p_fa) less than 1%.

Proposed herein is a framework between disparate multiple sensing modalities with correlated biometrics between the orthogonal modalities to aide in actor identification and discrimination within a particular defined scene. For the purposes of detection and classification, there are advantages and disadvantages to using either seismic or ultrasonic-sensing modalities individually to detect, identify, and classify successfully a subject to some degree. The seismic modality provides a clean, separable set of results for walkers close to the sensor but in general fails to be as successful at a larger stand-off due to ground wave attenuation and provides only some level of detection. The ultra-sonic modality provides stronger detection results from a greater stand-off distance in direct comparison to the seismic sensor due to higher fidelity of the signal. However, the resulting signature is harder to separate into individual walk-cycle events due to the broadband nature of the ultrasonic profile. In light of the advantages and disadvantages, it is desirable to utilize the capabilities of both sensors to create a framework for a robust singular solution for detection, identification, and classification of the actor(s) within the scene. In this particular case, data fusion techniques such as Hidden Markov Models and Dempster Shafer Theory provide a framework and methodology for exploiting each modality in an optimized and complementary fashion. In a probabilistic sense, this means that the chance of a successful detection is increased through a decision model that uses both the seismic and ultrasonic detection results to detect a target with high confidence.

In order to realize the wide-scale deployment of high-endurance, unattended mobile sensing technologies, it is vital to ensure the self-preservation of the sensing assets. Deployed mobile sensor nodes face a variety of physical security threats including theft, vandalism and physical damage. Unattended mobile sensor nodes must be able to respond to these threats with control policies that facilitate escape and evasion to a low-risk state. In this work the Precision Immobilization Technique (PIT) problem has been considered. The PIT maneuver is a technique that a pursuing, car-like vehicle can use to force a fleeing vehicle to abruptly turn ninety degrees to the direction of travel. The abrupt change in direction generally causes the fleeing driver to lose control and stop. The PIT maneuver was originally developed by law enforcement to end vehicular pursuits in a manner that minimizes damage to the persons and property involved. It is easy to imagine that unattended autonomous convoys could be targets of this type of action by adversarial agents. This effort focused on developing control policies unattended mobile sensor nodes could employ to escape, evade and recover from PIT-maneuver-like attacks. The development of these control policies involved both simulation as well as small-scale experimental testing. The goal of this work is to be a step toward ensuring the physical security of unattended sensor node assets.

We illustrate an approach for adapting the configuration of surveillance-based distributed sensor fields. By using a robust method of centralized adaptation of the field configuration, the performance of distributed sensor fields can be greatly extended. In this paper, we use the concept of Pareto optimality (that is, the achievement of desired tradeoffs between competing objectives) as a mechanism for describing the intent of the original deployment. We illustrate a computational approach to adaptation of the field configuration, provide numerical examples, and provide an analysis of the stability properties.

This paper discusses the many features and composed technologies in Firestorm™ - a Distributed Collaborative Fires and Effects software. Modern response management systems capitalize on the capabilities of a plethora of sensors and its output for situational awareness. Firestorm utilizes a unique networked lethality approach by integrating unmanned air and ground vehicles to provide target handoff and sharing of data between humans and sensors. The system employs Bayesian networks for track management of sensor data, and distributed auction algorithms for allocating targets and delivering the right effect without information overload to the Warfighter. Firestorm Networked Effects Component provides joint weapon-target pairing, attack guidance, target selection standards, and other fires and effects components. Moreover, the open and modular architecture allows for easy integration with new data sources. Versatility and adaptability of the application enable it to devise and dispense a suitable response to a wide variety of scenarios. Recently, this application was used for detecting and countering a vehicle intruder with the help of radio frequency spotter sensor, command driven cameras, remote weapon system, portable vehicle arresting barrier, and an unmanned aerial vehicle - which confirmed the presence of the intruder, as well as provided lethal/non-lethal response and battle damage assessment. The completed demonstrations have proved Firestorm's™ validity and feasibility to predict, detect, neutralize, and protect key assets and/or area against a variety of possible threats. The sensors and responding assets can be deployed with numerous configurations to cover the various terrain and environmental conditions, and can be integrated to a number of platforms.

We describe the design, fabrication and characterization of simple micromechanical structures that are capable of sensing static electric time varying electromagnetic fields. Time varying electric field sensing is usually achieved using an electromagnetic antenna and a receiver. However, these antenna-based approaches do not exhibit high sensitivity over a broad frequency (or wavelength) range. An important aspect of the present work is that, in contrast to traditional antennas, the dimensions of these micromechanical oscillators can be much smaller than the wavelength of the electromagnetic wave. We characterized the fabricated micromechanical oscillators by measuring their responses to time varying electric and electromagnetic fields.

As camouflage systems become increasingly sophisticated in their potential to conceal military personnel and precious cargo, evaluation methods need to evolve as well. This paper presents an overview of one such attempt to explore alternative methods for empirical evaluation of dynamic camouflage systems which aspire to keep pace with a soldier's movement through rapidly changing environments that are typical of urban terrain. Motivating factors are covered first, followed by a description of the Blitz Camouflage Assessment (BCA) process and results from an initial proof of concept experiment conducted in November 2006. The conclusion drawn from these results, related literature and the author's personal experience suggest that operational evaluation of personal camouflage needs to be expanded beyond its foundation in signal detection theory and embrace the challenges posed by high levels of cognitive processing.

Visible and solar blind UV detector arrays with spectrum below visible light or solar blind avoid interference from visible light and solar radiation. A candidate for UV detectors is ZnO with a high bandgap of 3.4 eV. ZnO with combinations of Mg or Cd can be tuned to absorb ultraviolet light from 210 nm to 450 nm. Banpil is developing visible and solar blind photodetectors using ZnO nanowires grown at temperatures below 400C to facilitate direct ZnO growth on silicon ROICs providing maximum photon absorption. Potential candidates to combine with ZnO to increase the bandgap potential are MgO and BeO. Solution methods were developed to grow ZnBeO nanowires and solution methods to overcome the ZnO wurtzite, MgO cubic and miscibility gap to grow ZnMgO nanowires. Modeling, simulation, process steps to grow ZnBeO and ZnMgO using electrophoresis and final measurement results are shown for generating ZnBeO and ZnMgO nanowire solar and visible blind sensors.

Next Generation EO/IR focal plane arrays using nanostructure materials are being developed for a variety of Defense Applications including Unattended Ground Sensor Applications. Several different nanomaterials are being evaluated for these applications. These include ZnO nanowires that have demonstrated large signal to noise ratio as a wide band gap nanostructure material in the UV band. Similarly, the work is under way using Carbon Nanotubes (CNT) for a high speed detector and focal plane array as bolometer for IR bands of interest, which can be implemented for the unattended ground sensor applications. In this paper, we will discuss the sensor design and model predicting performance of an EO/IR focal plane array that can cover the UV to IR bands of interest. The model can provide a robust means for comparing performance of the EO/IR FPA's and Sensors that can operate in the UV, Visible-NIR (0.4-1.8μ), SWIR (2.0-2.5μ), MWIR (3-5μ), and LWIR bands (8-14μ). This model can be used as a tool for predicting performance of nanostructure arrays under development. We will also discuss our results on growth and characterization of ZnO nanowires and CNT's for the next generation sensor applications. Several approaches for compact energy harvesting using nanostructures will be discussed.

In many situations where it is necessary to set up a communication link such as emergencies or in remote locations, running fiber between two sites is not practical. Free-space optics (FSO) holds the potential for high bandwidth communication in such situations with relatively low cost, low maintenance, quick installation times, and average 70- 80% connectivity. Since atmospheric conditions can significantly affect the capability of this type of communication system to transfer information consistently and operate effectively, the effects of atmosphere on FSO communication and consequent optimal wavelength range for transmission are investigated through MODTRAN-based modeling of 1.55 μm transmission. Simulations were performed for multiple elevation angles in atmospheric weather conditions including clear maritime, desert extinction, and various levels of rain and fog to simulate surface-to-surface and surfaceto- air FSO communication networks. Atmospheric, free-space, and scintillation losses are analyzed for optical path lengths of up to 2 km or greater to determine minimum transmit power required for successful data reception. In addition, the effects of atmospheric turbulence on beam propagation in the evaporation layer are investigated, where wavefront sensing with adaptive optics as well as a software Kalman filter are seen as a means to compensate for wavefront distortion. Using advanced laser sources to provide illumination at infrared wavelengths, particularly around the eye-safe 1.55 μm wavelength, it should be possible to overcome many transmission limitations associated with atmospheric conditions such as adverse weather and turbulence to enable high data rate communication links where the use of fiber is not practical or prohibited.

A prototype magnetometer for anti-submarine warfare applications is being developed based on nonlinear magneto-optical rotation (NMOR) in atomic vapors. NMOR is an atomic spectroscopy technique that exploits coherences among magnetic sublevels of atoms such as cesium or rubidium to measure magnetic fields with high precision. NMOR uses stroboscopic optical pumping via frequency or amplitude modulation of a linearly polarized laser beam to create the alignment. An anti-relaxation coating on the walls of the atomic vapor cell can result in a long lifetime of 1 s or more for the coherence and enables precise measurement of the precession frequency. With proper feedback, the magnetometer can self-oscillate, resulting in accurate tracking and fast time response. The NMOR magnetic resonance spectrum of ⁸⁷Rb has been measured as a function of heading in Earth's field. Optical pumping of alignment within the F=2 hyperfine manifold generates three resonances separated by the nonlinear Zeeman splitting. The spectra show a high degree of symmetry, consisting of a central peak and two side peaks of nearly equal intensity. As the heading changes, the ratio of the central peak to the average of the two side peaks changes. The amplitudes of the side peaks remain nearly equal. An analysis of the forced oscillation spectra indicates that, away from dead zones, heading error in self-oscillating mode should be less than 1 nT. A broader background is also observed in the spectra. While this background can be removed when fitting resonance spectra, understanding it will be important to achieving the small heading error in self-oscillating mode that is implied by the spectral measurements. Progress in miniaturizing the magnetometer is also reported. The new design is less than 10 cm across and includes fiber coupling of light to and from the magnetometer head. Initial tests show that the prototype has achieved a narrow spectral width and a strong polarization rotation signal.