This Pdf file contains the Front Matter associated with SPIE Proceedings Volume 7693, including title page, copyright information, table of contents, conference committee listing, and introduction.

The electronic states in trivalent rare-earth ions offer an excellent opportunity for designing efficient fibre and bulk lasers for atmospheric remote sensing and LIDAR technology. The first part of this review article focuses on engineering passive fibres for 2-5 μm transmission. The fabrication of single-mode tungsten tellurite optical fibres from high purity bulk glass rods is described. Fibres with a numerical aperture of 0.1 and core diameter of 10±2 μm have been drawn using suction and rod-in-tube techniques of fibre preform fabrication. Losses of 2.3 dBm-1 at 974 nm have been measured. These fibres are of interest for use as passive transmission fibre or active fibres for lasing in the 2-5 μm spectral region. The use of heavy metal oxides in the fabrication of glass leads to extended infrared transmission. In the 2^nd part of the review we compare the slope efficiencies of Tm³⁺ and Tm³⁺/Ho³⁺ doped glasses and fibres for 1950-2080 nm lasers using a range of pumping schemes in tellurite and germanium oxide hosts. We also explain the importance of choosing 1950-2080 nm tunable lasers as a pump source for stimulated emission at longer wavelengths.

Significant advances have recently been made to develop optically interrogated microsensor based chemical sensors with specific application to hydrogen vapor sensing and leak detection in the hydrogen economy. We have developed functionalized polymer-film and palladium/silver alloy coated microcantilever arrays with nanomechanical sensing for this application. The uniqueness of this approach is in the use of independent component analysis (ICA) and the classification techniques of neural networks to analyze the signals produced by an array of microcantilever sensors. This analysis identifies and quantifies the amount of hydrogen and other trace gases physisorbed on the arrays. Selectivity is achieved by using arrays of functionalized sensors with a moderate distribution of specificity among the sensing elements. The device consists of an array of beam-shaped transducers with molecular recognition phases (MRPs) applied to one surface of the transducers. Bending moments on the individual transducers can be detected by illuminating them with a laser or an LED and then reading the reflected light with an optical position sensitive detector (PSD) such as a CCD. Judicious selection of MRPs for the array provides multiple isolated interaction surfaces for sensing the environment. When a particular chemical agent binds to a transducer, the effective surface stresses of its modified and uncoated sides change unequally and the transducer begins to bend. The extent of bending depends upon the specific interactions between the microcantilever's MRP and the analyte. Thus, the readout of a multi-MRP array is a complex multidimensional signal that can be analyzed to deconvolve a multicomponent gas mixture. The use of this sensing and analysis technique in unattended networked arrays of sensors for various monitoring and surveillance applications is discussed.

Chalcogenide glass fibers are the best candidates for mid-infrared transmission. Their low optical losses and high-power damage threshold are enabling numerous applications: laser power delivery, chemical sensing and imaging. Furthermore, chalcogenide glass fibers are best candidates for demonstrating rare-earth doped fiber lasers and supercontinuum sources in the mid-infrared. The latest results towards the creation of a 4.5 micron fiber laser and a broadband (2-5 micron) supercontinuum source are presented.

Next Generation EO/IR focal plane arrays using nanostructure materials are being developed for a variety of Defense Applications including Unattended Ground Sensor 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.

Imagery has proven to be a valuable complement to Unattended Ground Sensor (UGS) systems. It provides ultimate verification of the nature of detected targets. However, due to the power, bandwidth, and technological limitations inherent to UGS, sacrifices have been made to the imagery portion of such systems. The result is that these systems produce lower resolution images in small quantities. Currently, a high resolution, wireless imaging system is being developed to bring megapixel, streaming video to remote locations to operate in concert with UGS. This paper will provide an overview of how using Wifi radios, new image based Digital Signal Processors (DSP) running advanced target detection algorithms, and high resolution cameras gives the user an opportunity to take high-powered video imagers to areas where power conservation is a necessity.

A research-oriented Army Technology Objective (ATO) named Sensor and Information Fusion for Improved Hostile Fire Situational Awareness uniquely focuses on the underpinning technologies to detect and defeat any hostile threat; before, during, and after its occurrence. This is a joint effort led by the Army Research Laboratory, with the Armaments and the Communications and Electronics Research, Development, and Engineering Centers (CERDEC and ARDEC) partners. It addresses distributed sensor fusion and collaborative situational awareness enhancements, focusing on the underpinning technologies to detect/identify potential hostile shooters prior to firing a shot and to detect/classify/locate the firing point of hostile small arms, mortars, rockets, RPGs, and missiles after the first shot. A field experiment conducted addressed not only diverse modality sensor performance and sensor fusion benefits, but gathered useful data to develop and demonstrate the ad hoc networking and dissemination of relevant data and actionable intelligence. Represented at this field experiment were various sensor platforms such as UGS, soldier-worn, manned ground vehicles, UGVs, UAVs, and helicopters. This ATO continues to evaluate applicable technologies to include retro-reflection, UV, IR, visible, glint, LADAR, radar, acoustic, seismic, E-field, narrow-band emission and image processing techniques to detect the threats with very high confidence. Networked fusion of multi-modal data will reduce false alarms and improve actionable intelligence by distributing grid coordinates, detection report features, and imagery of threats.

We show design and performance results for an Unattended Ground Sensors (UGS) Automatic Target Recognition (ATR) target classifier using infrared (IR) imagery. Our goal was to develop a basic ATR capability to separate human vs. animal vs. vehicle vs. non-target. Our current UGS video capability accurately detects tracks and transmits targetcentered long wave infrared and visible imagery to a base station. We demonstrate an ATR capability to classify and transmit only targets of interest to the user while excluding others. We describe the ATR development process which includes data collection, building a truthed dataset, feature development, classifier training and performance evaluation.

In this paper, an offline learning mechanism based on the genetic algorithm is proposed for autonomous vehicles to emulate human driver behaviors. The autonomous driving ability is implemented based on a Prediction- and Cost function-Based algorithm (PCB). PCB is designed to emulate a human driver's decision process, which is modeled as traffic scenario prediction and evaluation. This paper focuses on using a learning algorithm to optimize PCB with very limited training data, so that PCB can have the ability to predict and evaluate traffic scenarios similarly to human drivers. 80 seconds of human driving data was collected in low-speed (< 30miles/h) car-following scenarios. In the low-speed car-following tests, PCB was able to perform more human-like carfollowing after learning. A more general 120 kilometer-long simulation showed that PCB performs robustly even in scenarios that are not part of the training set.

The reliability of individual robots influences the success of multirobot missions. When one robot fails, others must be retasked to complete the failed robot's tasks. This increases the failure likelihood for these other robots. Existing multirobot task allocation systems consider robot failures only after the fact, via replanning. In this paper we show that mission performance for multirobot missions can be improved by using knowledge of robot failure rates to inform the initial task allocation.

We have developed algorithms to automatically learn a detection map of a deployed sensor field for a virtual presence and extended defense (VPED) system without apriori knowledge of the local terrain. The VPED system is an unattended network of sensor pods, with each pod containing acoustic and seismic sensors. Each pod has the ability to detect and classify moving targets at a limited range. By using a network of pods we can form a virtual perimeter with each pod responsible for a certain section of the perimeter. The site's geography and soil conditions can affect the detection performance of the pods. Thus, a network in the field may not have the same performance as a network designed in the lab. To solve this problem we automatically estimate a network's detection performance as it is being installed at a site by a mobile deployment unit (MDU). The MDU will wear a GPS unit, so the system not only knows when it can detect the MDU, but also the MDU's location. In this paper, we demonstrate how to handle anisotropic sensor-configurations, geography, and soil conditions.

This paper describes the development of linear pyroelectric array systems for classification of human, animal, and vehicle targets. The pyroelectric array is simulated to produce binary profiles of targets. The profiles are classified based on height to width ratio using Naïve Bayesian classifiers. Profile widths of targets can vary due to the speed of the target. Target speeds were calculated using two techniques; two array columns, and a tilted array. The profile width was modified by the calculated speeds to show an improvement in classification results.

A priori information on suspicious behaviour is extremely valuable for countering threats involving improvised explosive devices (IEDs). Suspicious activities along routes during expeditionary operations can be monitored by unattended networks using simple sensing nodes that can gather data for continuous monitoring of daily vehicle activity. Dedicated software yields the necessary intelligence on these activities by filtering suspicious behaviour from anomalous behaviour (including false alarms). Research has started to equip a commercially available sensor network with data analysis software. It aims at demonstrating the detection of suspicious behaviour along roads, within a required time span. Three phases are distinguished. First phase is the analysis of traffic flux in a simple scenario with three networks lying at three junctions. The second phase investigates the ability to track and classify one object in this scenario, while the third phase aims to track and classify two or more objects. Findings are presented for phase one, flux measurements.A priori information on suspicious behaviour is extremely valuable for countering threats involving improvised explosive devices (IEDs). Suspicious activities along routes during expeditionary operations can be monitored by unattended networks using simple sensing nodes that can gather data for continuous monitoring of daily vehicle activity. Dedicated software yields the necessary intelligence on these activities by filtering suspicious behaviour from anomalous behaviour (including false alarms). Research has started to equip a commercially available sensor network with data analysis software. It aims at demonstrating the detection of suspicious behaviour along roads, within a required time span. Three phases are distinguished. First phase is the analysis of traffic flux in a simple scenario with three networks lying at three junctions. The second phase investigates the ability to track and classify one object in this scenario, while the third phase aims to track and classify two or more objects. Findings are presented for phase one, flux measurements.

We explore the options in deployment of fiber-optic cables as seismic sensors, a technology that has only become practical recently. Cables provide sensing capabilities along their lengths and thus provide a fundamentally different capability than the traditional point sensors. Furthermore, cables can have gradual curves, and this enables deployments to be fit precisely to sensing needs such as irregularities in the terrain. We discuss grid deployments and contrast them with curving deployments such as regular and irregular spirals. We discuss how to manage deployments over terrain with varying degrees of interest for monitoring. We then discuss some of the processing challenges in analyzing data where one dimension (distance) is much more precise than another (time of occurrence). Key concerns are in detecting changes in speed and direction, which can be tracked if processors hand off data to one another at known turn points such as road intersections. We discuss a bent-cable deployment that can facilitate such tracking.

DARPA has initiated a program to explore persistent presence in the deep ocean. The deep ocean is difficult to access and presents a hostile environment. Persistent operations in the deep ocean will require new technology for energy, communications and autonomous operations. Several fundamental characteristics of the deep ocean shape any potential system architecture. The deep sea presents acoustic sensing opportunities that may provide significantly enhanced sensing footprints relative to sensors deployed at traditional depths. Communication limitations drive solutions towards autonomous operation of the platforms and automation of data collection and processing. Access to the seabed presents an opportunity for fixed infrastructure with no important limitations on size and weight. Difficult access and persistence impose requirements for long-life energy sources and potentially energy harvesting. The ocean is immense, so there is a need to scale the system footprint for presence over tens of thousands and perhaps hundreds of thousands of square nautical miles. This paper focuses on the aspect of distributed sensing, and the engineering of networks of sensors to cover the required footprint.

The US has over 58,000 miles of ocean shoreline, over 5500 hundred miles of Great Lakes shoreline, and over 3,500,000 miles of river and small lake shoreline. These waterways are critical to the nation's strategic, economic and societal well being. These assets must be protected from potential terrorist attacks. It is a daunting task for an open society to protect such a large and distributed area while still preserving the freedoms for its citizens to enjoy the natural beauty of our waterways. The US has a well developed fleet of merchant tugs and barges that engage in day to day commercial activity around the coasts, rivers and lakes of the country. This paper will discuss the notion of developing a nationwide mobile sensor network by equipping these barges and tugs with sensor suites that would feed data into a common operations' center. The data will be displayed to the first responder community and the vessel operators via data streams from Rite-View (a robust 3D modeling and simulation tool).

Use of sensor systems in water bodies has applications that range from environmental and oceanographic research to port and homeland security. Power sources are often the limiting component for further reduction of sensor system size and weight. We present recent investigations of metal-anode water-activated galvanic cells, specifically water-activated Alcells using inorganic alkali peroxides and solid organic oxidizers (heterocyclic halamines), in a semi-fuel cell configuration (i.e., with cathode species generated in situ and flow-through cells). The oxidizers utilized are inexpensive solid materials that are generally (1) safer to handle than liquid solutions or gases, (2) have inherently higher current and energy capacity (as they are not dissolved), and, (3) if appropriately packaged, will not degrade over time. The specific energy (S.E.) of Al-alkali peroxide was found to be 230 Wh/kg (460 Wh/kg, considering only active materials) in a seven-gram cell. Interestingly, when the cell size was increased (making more area of the catalytic cathode electrode available), the results from a single addition of water in an Al-organic oxidizer cell (weighing ~18 grams) showed an S.E. of about 200 Wh/kg. This scalability characteristic suggests that values in excess of 400 Wh/kg could be obtained in a semi-fuel-cell-like system. In this paper, we also present design considerations that take into account the energy requirements of the pumping devices and show that the proposed oxidizers, and the possible control of the chemical equilibrium of these cathodes in solution, may help reduce this power requirement and hence enhance the overall energetic balance.

The performance of a distributed sensor field that seeks to achieve surveillance coverage against moving targets is tied to many different constraints and competing objectives. Understanding these tradeoffs is paramount if such systems are to be efficiently designed and employed. When physically deploying the sensor field, the placement of sensors in specific patterns can tremendously alter the system's performance relative to these tradeoffs. Given a robust parameterization of the field configuration, the tradeoffs manifest themselves as boundaries in the space of achievable parameter combinations under the system's constraints. In this paper, we utilize a numerical Pareto optimization approach to derive explicit tradeoff surfaces for the problem of a cooperative network of passive sensor nodes, illustrating the tradeoff between improving field-level search performance and decreasing false searches. We conclude by discussing the impact of these results on the deployment of sensor fields.

Methods of human classification and direction of travel are developed for the purpose of being embedded in low-power, low-cost microprocessors. Techniques are explored for classifying an impulsive set of events in a seismic field as being either human or non-human based on information extrapolated from time-domain data of geophones. Additionally, a method of time domain direction of travel determination is explored. As a target is traversing the field of detection, simple impulse detection techniques determine seismic activities that are of interest. By recreating the time-domain signal as an average energy over time, the frequency of footstep of the target can be determined after a human has left the field by using post processing techniques, even when multiple targets are present. An autocorrelation of the energy averaged signal will yield an output that can be used to easily determine the most dominant frequency of the observed series of impulsive events. This method is capable of classifying humans under certain conditions at a rate of up to 98% with a varying rate of rejection for different types of animals and environmental factors. The technique can be easily integrated to work in conjunction with other modalities for an increase in classifier confidence.

A novel examination of a method capable of providing situational awareness of sniper fire from small arms fire is presented. Situational Awareness (SA) information is extracted by exploiting two distinct sounds created by small arms discharge: the muzzle blast (created when the bullet leaves the barrel of the gun) and the shockwave (sound created by a supersonic bullet). The direction of arrival associated with the muzzle blast will always point in the direction of the shooter. Range can be estimated from the muzzle blast alone, however at greater distances geometric dilution of precision will make obtaining accurate range estimates difficult. To address this issue, additional information obtained from the shockwave is utilized in order to estimate range to shooter. The focus of the paper is the development of a shockwave propagation model, the development of ballistics models (based off empirical measurements), and the subsequent application towards methods of determining shooter position. Knowledge of the rounds ballistics is required to estimate range to shooter. Many existing methods rely on extracting information from the shockwave in an attempt to identify the round type and thus the ballistic model to use ([1]). It has been our experience that this information becomes unreliable at greater distances or in high noise environments. Our method differs from existing solutions in that classification of the round type is not required, thus making the proposed solution more robust. Additionally, we demonstrate that sufficient accuracy can be achieved without the need to classify the round.

A self-oscillating magnetometer based on nonlinear magneto-optical rotation using amplitude-modulated pump light and unmodulated probe light (AM-NMOR) in ⁸⁷Rb has been constructed and tested towards a goal of airborne detection of magnetic anomalies. In AM-NMOR, stroboscopic optical pumping via amplitude modulation of the pump beam creates alignment of the ground electronic state of the rubidium atoms. The Larmor precession causes an ac rotation of the polarization of a separate probe beam; the polarization rotation frequency provides a measure of the magnetic field. An anti-relaxation coating on the walls of the atomic vapor cell results in a long lifetime of 56 ms for the alignment, which enables precise measurement of the precession frequency. Light is delivered to the magnetometer by polarization-maintaining optical fibers. Tests of the sensitivity include directly measuring the beat frequency between the magnetometer and a commercial instrument and measurements of Earth's field under magnetically quiet conditions, indicating a sensitivity of at least 5 pT/νHz. Rotating the sensor indicates a heading error of less than 1 nT, limited in part by residual magnetism of the sensor.

Environmental sensor nodes support services from environmental stewardship to national security and defense. Expansion of high density sensor networks has been inhibited by the poor availability and high cost of long-term power sources. Trophos Energy demonstrates its own wireless environmental sensor network, entitled BackyardNet^TM, powered by Terrestrial Microbial Fuel Cell (TMFC) technology. When used in conjunction with Trophos' power management electronics, TMFCs offer the potential for robust, long-term power solutions that can service a variety of remote monitoring applications. This paper discusses the technical aspects of the BackyardNet^TM demonstration and the assessed viability of TMFC technology as robust power sources for remote applications.

Cell-based biosensors (CBBs) have important applications in biosecurity and rapid diagnostics. Current CBB technologies have challenges including cell immobilization on the sensors, high throughput fabrication and portability, and rapid detection of responses to environmental changes. We address these challenges by developing an integrated CBB platform that merges cell printing technology, a lensless charge-coupled device (CCD) imaging system, and custom-developed cell image processing software. Cell printing was used to immobilize cells within hydrogel droplets and pattern these droplets on a microfluidic chip. The CCD was used to detect the morphological response of the immobilized cells to external stimuli (e.g., environmental temperature change) using lensless shadow images. The morphological information can be also detected by sensing a small disturbance in cell alignment, i.e., minor alignment changes of smooth muscles cells on the biosensors. The automatic cell alignment quantification software was used to process the cell images (microscopic image was used as an example) and calculate the cell orientation in seconds. The same images were also manually processed as a control to validate and characterize the integrated platform functionality. The results showed software can measure the cell morphology (i.e., orientation) in an automated way without the need for labeling (e.g., florescent staining). Such an integrated CBB system will allow fabrication of CBBs at high throughput as well as rapidly monitor and measure morphological cellular responses.

The paper deals with Unattended Ground Sensors (UGS) and takes into consideration both present and future aspects of the practical deployment of this equipment under conditions of Electronic Warfare (EW), including the integration of UGS into a joint system using the Unmanned Aircraft System (UAS). The first part of the paper deals with the possibilities, characteristics and useable properties of seismic-acoustic communication in the group of nodes, supplementing the information coverage of existing UGS, including the selection of a suitable working frequency band for seismic communication. The second part of the paper then describes an alternative method of communication between nodes and UGS using LF radio communication, and analyses the design and real properties of a proposed communication channel in LF band, the design of a loop antenna and its mechanical construction. The interim conclusions of each section generalize the results of seismic-acoustic and radio LF communications as verified in practice, and describe both the advantages and disadvantages of communication channels defined in this way. The third part of the paper deals with the possibility of integrating the nodes-UGS to a central system consisting of a UAS device. It covers the design and an energy evaluation of a system operating on the principle of data selection from UGS. In addition, the paper includes illustrative photographs of the practical design and graphic results of real measurements.

Free-space optics (FSO) holds the potential for high bandwidth communication, but atmospheric conditions can significantly affect the capability of a communication system to transfer information successfully. 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 for multiple elevation angles in atmospheric conditions including clear maritime, desert extinction, and various levels of rain and fog. Beam transmission was also simulated for different relevant elevations for surface-to-surface and surface-to-air free-space optical communication networks. The atmospheric, free-space, and scintillation losses are investigated for optical path lengths of 2 km to determine transmit power required for successful data reception. In addition, FSO transmitter and receiver circuits were designed to optically relay an analog video signal and tested at path distances of up to 130 m. Using advanced tunable laser sources to provide illumination across wavelength ranges, particularly around the eye-safe 1.55 μm wavelength, it should be possible to overcome transmission limitations associated with adverse weather and atmospheric conditions.

Social networks generally provide an implementation of some kind of groups or communities which users can voluntarily join. Twitter does not have this functionality, and there is no notion of a formal group or community. We propose a method for identification of communities and assignment of semantic meaning to the discussion topics of the resulting communities. Using this analysis method and a sample of roughly a month's worth of Tweets from Twitter's "gardenhose" feed, we demonstrate the discovery of meaningful user communities on Twitter. We examine Twitter data streaming in real time and treat it as a sensor. Twitter is a social network which pioneered microblogging with the messages fitting an SMS, and a variety of clients, browsers, smart phones and PDAs are used for status updates by individuals, businesses, media outlets and even devices all over the world. Often an aggregate trend of such statuses may represent an important development in the world, which has been demonstrated with the Iran and Moldova elections and the anniversary of the Tiananmen in China. We propose using Twitter as a sensor, tracking individuals and communities of interest, and characterizing individual roles and dynamics of their communications. We developed a novel algorithm of community identification in social networks based on direct communication, as opposed to linking. We show ways to find communities of interest and then browse their neighborhoods by either similarity or diversity of individuals and groups adjacent to the one of interest. We use frequent collocations and statistically improbable phrases to summarize the focus of the community, giving a quick overview of its main topics. Our methods provide insight into the largest social sensor network in the world and constitute a platform for social sensing.

The unattended ground sensors (UGS) have come a long way over the more than 40 years they have been used to detect adversarial activities. From large, single phenomenology sensors with little signal processing and point to point communications the technology has now changed to small, intelligent sensors using network communications. This technology change has resulted in far more capable sensors but challenges remain for UGS to be effective in providing information to users.

Progress in several technical areas is being leveraged to advantage in Unattended Ground Sensor (UGS) systems. This paper discusses advanced technologies that are appropriate for use in UGS systems. While some technologies provide evolutionary improvements, other technologies result in revolutionary performance advancements for UGS systems. Some specific technologies discussed include wireless cameras and viewers, commercial PDA-based system programmers and monitors, new materials and techniques for packaging improvements, low power cueing sensor radios, advanced long-haul terrestrial and SATCOM radios, and networked communications. Other technologies covered include advanced target detection algorithms, high pixel count cameras for license plate and facial recognition, small cameras that provide large stand-off distances, video transmissions of target activity instead of still images, sensor fusion algorithms, and control center hardware. The impact of each technology on the overall UGS system architecture is discussed, along with the advantages provided to UGS system users. Areas of analysis include required camera parameters as a function of stand-off distance for license plate and facial recognition applications, power consumption for wireless cameras and viewers, sensor fusion communication requirements, and requirements to practically implement video transmission through UGS systems. Examples of devices that have already been fielded using technology from several of these areas are given.

UGS usage has transitioned from military applications in Vietnam directed at detecting enemy movements to much more sophisticated applications for classifying activity, tracking targets, identifying targets, and providing real time target response. The era of individual sensors has now moved into an era of collaborative sensors. The technologies behind this collaborative capability include networking many sensors together, performing complex processing of target features, integrating multiple sensing phenomenologies, fusing information from many sources, and presenting the user with advanced information visualization for events of interests. The influx of advanced technology will lead to a new era of ubiquitous UGS applications.

Smart sensors are critical components in distributed integrated systems and architectures. Continually improving sensors and increased requirements for surveillance have created end-to-end design and implementation challenges. The concurrent requirement to improve information sharing across organizations, in effect to network and share sensors, creates additional challenges. Distributing and managing the computational processing, end-to-end, in an implementation architecture poses new design, standardization, and interoperability challenges. With the emphasis on "smart," sensor-embedded processing is essential in unattended and high-performance applications and architectures. Distributed intelligent multimedia streaming and annotation technologies create opportunities to efficiently process, communicate, and dynamically adapt to mission requirements. This paper examines requirements drivers for smart sensor designs and explores implications for end-to-end architecture and sensor management. Additionally, it considers the need for standardization as an important enabler of networked capability, information assurance, and acquisition affordability.

Unattended Ground Sensors (UGS) are valuable tools for the U.S. military and border patrol, however, their utility is often limited due to their cost, size, and weight. Recently specific advances in micro power electronics, transducers, packaging, and signal processing techniques have enabled the development of a small, lightweight, and affordable UGS. Originally intended for small unit clearing/monitoring operations, a sensor has evolved to achieve detection performance comparable to state-of-the-art UGS. To meet a broader mission capability, battery life and detection capabilities have been extended and affordable networked cameras and repeaters have been developed. This paper will provide an overview of the key enabling technologies for affordable UGS, provide an overview and enhancements of this affordable UGS system, and review results of system testing.

This paper updates the improvements and benefits demonstrated in the next generation Northrop Grumman SCORPION II family of persistent surveillance and target recognition systems produced by the Xetron Campus in Cincinnati, Ohio. SCORPION II reduces the size, weight, and cost of all SCORPION components in a flexible, field programmable system that is easier to conceal and enables integration of over fifty different Unattended Ground Sensor (UGS) and camera types from a variety of manufacturers, with a modular approach to supporting multiple Line of Sight (LOS) and Beyond Line of Sight (BLOS) communications interfaces. Since 1998 Northrop Grumman has been integrating best in class sensors with its proven universal modular Gateway to provide encrypted data exfiltration to Common Operational Picture (COP) systems and remote sensor command and control. In addition to feeding 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 up to seven hundred SCORPION system gateways on single or multiple displays. This GUI enables a large amount of sensor data and imagery to be used for actionable intelligence as well as remote sensor command and control by a minimum number of analysts.

In the last two decades, research by McQ Inc. and others has led to substantial advances in the performance of unattended ground sensor (UGS) systems. These advancements include: extremely long battery life; small, robust packaging; high performance detection and classification algorithms; multimodal, multispectral sensors; long range communications; air droppable delivery; fully integrated sensor management; multi-sensor situational awareness and data fusion; advanced video detection and optical sensor development; and others. This research has demonstrated that there is a great deal that existing technology can do to solve users' intelligence, surveillance, and reconnaissance (ISR) requirements using UGS sensors. However, in spite of these advances, UGS systems have not enjoyed widespread use in either DoD, DHS or with law enforcement agencies (LEA). Although requirements differ from agency to agency and application to application, the primary factor that limits more widespread use of UGS systems is cost. Cost determines how many sensors an agency can buy and how they are used operationally. Only when sensors cost $100 or less will they be considered truly disposable. The focus of this paper is to present the technical considerations and a roadmap for producing truly low cost UGS sensors. Achieving this goal will then create within the DoD, DHS, LEA and other communities' requirements for UGS systems intended for a wide variety of uses that were never seriously considered previously because of cost. The implications are significant and will lead to an explosion in the number of UGS systems made and used on an annual basis.