This PDF file contains the front matter associated with SPIE Proceedings Volume 7112, including the Title Page, Copyright information, Table of Contents, Introduction, and the Conference Committee listing.

In this paper we propose system architecture for providing direction and dissemination in military environments. We start with a description of the problem of direction and dissemination. We then present our high level architecture and describe the functions of the main system components on which we focus. This includes the types of information and means by which they may be delivered, the filtering and fusion engines employed to focus and limit the information sent to each personnel, and the schedulers used to determine the order of delivery. We consider a structure that includes sending information directly to personnel, or depending on bandwidth and delay constraints, sending meta-information to personnel to assist in self-retrieval of information from a peer-to-peer network of sensors and other personnel. We illustrate the operation of the architecture using a specific military scenario.

We show how the performance of a sensor management algorithm can be improved by using the continuous transferable belief model (cTBM). Bayesian approaches can have problems modelling uncertainty whereas the transferable belief model (TBM) has been proven effective in dealing with this - the TBM achieves this by assigning support to sets of events rather than just singleton events. The discrete nature of such set theoretic uncertain reasoning approaches (including Dempster-Shafer approaches) can have problems modelling continuous signals such as the speed of a target; the cTBM has been developed to overcome such inherent problems. Existing work at Cardiff University classifies targets by combining the cTBM and a particle filter; each particle is used to construct a set of beliefs, which are then fused with the existing beliefs for classification - this is then used to update the particle filter. Williams et al. provide a framework for managing sensor networks by balancing the quality of information gained from a sensor network with the required communications cost when tracking a target. Our proposed new system integrates the above approaches, and has similar basic communications costs to the latter. It is now not only able to track a target but also classify it - the combination results in improved performance; this is shown in our simulation results from Monte Carlo trials with various scenarios.

A sensor network in the field is usually required to support multiple sensing tasks or missions to be accomplished simultaneously. Since missions might compete for the exclusive usage of the same sensing resource we need to assign individual sensors to missions. Missions are usually characterized by an uncertain demand for sensing resource capabilities. In this paper we model this assignment problem by introducing the Sensor Utility Maximization (SUM) model, where each sensor-mission pair is associated with a utility offer. Moreover each mission is associated with a priority and with an uncertain utility demand. We also define the benefit or profit that a sensor can bring to a mission as the fraction of mission's demand that the sensor is able to satisfy, scaled by the priority of the mission. The goal is to find a sensor assignment that maximizes the total profit, while ensuring that the total utility cumulated by each mission does not exceed its uncertain demand. SUM is NP-Complete and is a special case of the well known Generalized Assignment Problem (GAP), which groups many knapsack-style problems. We compare four algorithms: two previous algorithms for problems related to SUM, an improved implementation of a state-of-the-art pre-existing approximation algorithm for GAP, and a new greedy algorithm. Simulation results show that our greedy algorithm appears to offer the best trade-off between quality of solution and computation cost.

TASTE is a software tool for specifying and deploying unattended ground sensors (UGS) in a composition which the commander assumes will suit his needs the best. With TASTE different sensor types such as acoustic, magnetic, seismic, radar and IR imaging sensors can be deployed virtually and their individual and combined performances analyzed. Sensors can be deployed in scenarios for neutral, friendly and enemy movements and a set of typical environmental conditions can be selected as input to the simulator. TASTE will play user defined scenario, calculating and monitoring the performance and behavior of the individual UGS sensors or of the entire sensor network.

Sub-sea monitoring of floating oil production platforms is a crucial issue in the interests of security, efficient functioning and pollution prevention. Sensors and sensor networks are essential tools for implementing the monitoring operations and low-error data communication from the sensors and within the network is a critical element in these systems. Free space optics (FSO) has gained recognition in numerous applications as a high bandwidth, energy efficient communication medium and has recently been considered as a viable alternative to acoustic communications for underwater applications when high data rates are required over short transmission ranges. Video recording is a powerful method for gathering extensive data in time and space that requires broadband communication facilities, such as could be provided by FSO. However, the immense variability of background illumination in shallow waters, inducing shot noise at the receiver, presents a challenge for sub-sea FSO. This has stimulated us to investigate the potential of underwater FSO in the UV solarblind spectral range. The potential of UV solarblind optical wireless links for sub-sea FSO is investigated in this paper and compared with performance at 520nm. In clear ocean data rates of 100bps can be transmitted over distances of above 120m using 520nm radiation, but this range is reduced to around 10m in harbour waters for these data rates and to 50m in clear ocean when the data rate is increased to 100Mbps. It is anticipated that ranges of 10m can also be obtained with UV solarblind wavelengths, although experimental corroboration is not yet available.

A new algorithm for producing DC balanced sets of holograms designed to be used on ferroelectric liquid crystal displays is presented in this article. The algorithm minimises Fourier plane intensity fluctuations while guarantees equal per-pixel phase states by exploiting phase shifting that are not be detected by photodetectors. It improves on the scrolling method employed for single beam steering by allowing complex beam patterns, such as multiple beams or beams with defocusing, to be DC balanced effectively. A proof of concept point-to-multipoint optical transmission system is presented, demonstrating the feasibility of obtaining good optical transmissions for complex beam shapes.

Free-space optics (FSO) technology utilizes a modulated light beam to transmit information through the atmosphere. Due to reduced size and cost, and higher data rates, FSO can be more effective than wireless communication. Although atmospheric conditions can affect FSO communication, a line-of-sight connection between FSO transceivers is a necessary condition to maintain continuous exchange of data, voice, and video information. To date, the primary concentration of mobile FSO research and development has been toward accurate alignment between two transceivers. This study introduces a fully automatic, advanced alignment system that will maintain a line of sight connection for any FSO transceiver system. A complete transceiver system includes a position-sensing detector (PSD) to receive the signal, a laser to transmit the signal, a gimbal to move the transceiver to maintain alignment, and a computer to coordinate the necessary movements during motion. The FSO system was tested for mobility by employing one gimbal as a mobile unit and establishing another as a base station. Tests were performed to establish that alignment between two transceivers could be maintained during a given period of experiments and to determine the maximum speeds tolerated by the system. Implementation of the transceiver system can be realized in many ways, including vehicle-to-base station communication or vehicle-to-vehicle communication. This study is especially promising in that it suggests such a system is able to provide high-speed data in many applications where current wireless technology may not be effective. This phenomenon, coupled with the ability to maintain an autonomously realigned connection, opens the possibility of endless applications for both military and civilian use.

High-resolution telescope systems used for observational tasks require sufficiently large apertures to enhance the spatial resolution. Due to the propagation through turbulent layers of the atmosphere the distorted wavefront implicates a broadening of the imaged spot and hence a loss in optical resolution. The improvement in visual resolution by applying adaptive optics has been successfully demonstrated in a mobile telescope platform. To compensate for the effects of atmospheric turbulence, a closed-loop system was developed with a bandwidth of up to 600 Hz capable to achieve a wavefront correction with a residual wavefront deformation of <50 nm RMS. A reference signal which is probing the wavefront distortion is realized with the help of a coherent laser beam emanating from the object. The developed adaptive optical system is capable of compensating phase distortions in a conjugated plane with time constants of 30 ms. Turbulence was artificially induced along the optical path by a turbulence generator. Measurements of MTF values and Strehl ratio will be presented.

This paper addresses the task of making a mosaic from images gathered by a down-looking camera on an airborne platform. This is in the context of a system to detect and map the positions of moving objects. We present three mosaicing approaches based on integrating together sets of measured pairwise homographies, i.e. geometric relationships, between overlapping image frames. The methods are simple chaining, consensus placement and bundle adjustment. We have demonstrated all the approaches with simulated data whilst the simplest way of using pairwise links, simple one-dimensional chaining, has been demonstrated with real data. In our bundle adjustment method, we use a two-dimensional network of pairwise links; when each frame is added to the mosaic, all the constituent frames are adjusted with respect to each other so that the consistency over the entire network is optimised. We have successfully shown, in simulation, that the bundle adjustment technique results in much more consistent, undistorted maps.

The strength of radar response varies considerably. In this regard, the dynamic range of most receivers is not sufficient enough to operate optimally. Due to this fact, radar signal may represent only a fraction of the real backscattering phenomena. One way to solve the problem is to use automatic gain control (AGC). It helps to prevent the saturation of responses but inflicts performance degradation on subsequent radar signal processing. The same problem with dynamic range exists in other fields of sensing as well. For example, a solution in digital photography is to use various exposure times to determine the most appropriate one for the current conditions. In this paper, a corresponding approach is proposed for analyzing radar responses. The method requires measurements of a selected area to be performed with various gains, and the resulting dynamic ranges should overlap partially. The use of a linear receiver ensures that both the power and the coherent phase statistics can be extracted from the data. Using the proposed approach, a few distributions derived from extensive land clutter recordings from Finnish landscape are presented.

A new generation of electric field sensors developed at the University of Sussex is enabling an alternative to contact voltage and non-contact magnetic field measurements. We have demonstrated the capability of this technology in a number of areas including ECG through clothing, remote off-body ECG, through wall movement sensing and electric field imaging. Clearly, there are many applications for a generic sensor technology with this capability, including long term vital sign monitoring. The non-invasive nature of the measurement also makes these sensors ideal for man/machine and human/robot interfacing. In addition, there are obvious security and biometric possibilities since we can obtain physiological data remotely, without the knowledge of the subject. This is a clear advantage if such systems are to be used for evaluating the psychological state of a subject. In this paper we report the results obtained with a new version of the sensor which is capable of acquiring electrophysiological signals remotely in an open unshielded laboratory. We believe that this technology opens up a new area of remote biometrics which could have considerable implications for security applications. We have also demonstrated the ability of EPS to function in closely-packed one and two dimensional arrays for real-time imaging.

With the ever present threat to commerce, both politically and economically, technological innovations provide a means to secure the transportation infrastructure that will allow efficient and uninterrupted freight-flow operations for trade. Currently, freight coming into United States ports is "spot checked" upon arrival and stored in a container yard while awaiting the next mode of transportation. For the most part, only fences and security patrols protect these container storage yards. To augment these measures, the authors propose the use of aerial surveillance vehicles equipped with video cameras and wireless video downlinks to provide a birds-eye view of port facilities to security control centers and security patrols on the ground. The initial investigation described in this paper demonstrates the use of unmanned aerial surveillance vehicles as a viable method for providing video surveillance of container storage yards. This research provides the foundation for a follow-on project to use autonomous aerial surveillance vehicles coordinated with autonomous ground surveillance vehicles for enhanced port security applications.

This paper addresses benefits derived from the universal gateway utilized in Northrop Grumman Systems Corporation's (NGSC) SCORPION, a persistent surveillance and target recognition system produced by the Xetron campus in Cincinnati, Ohio. SCORPION is currently deployed in Operations Iraqi Freedom (OIF) and Enduring Freedom (OEF). The SCORPION universal gateway is a flexible, field programmable system that provides integration of over forty Unattended Ground Sensor (UGS) types from a variety of manufacturers, multiple visible and thermal electro-optical (EO) imagers, and numerous long haul satellite and terrestrial communications links, including the Army Research Lab (ARL) Blue Radio. Xetron has been integrating best in class sensors with this universal gateway to provide encrypted data exfiltration to Common Operational Picture (COP) systems and remote sensor command and control since 1998. In addition to being fed to COP systems, SCORPION data can be visualized in the Common sensor Status (CStat) graphical user interface 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 user friendly visualization enables a large amount of sensor data and imagery to be used as actionable intelligence by a minimum number of analysts.

Monitoring national borders for illegal border crossing activity is a difficult task. Relying on border monitoring personnel patrolling large border areas as the sole solution is very taxing and expensive due to varying terrain and the need to monitor 24 hours every day. Augmenting personnel with technology is required to maintain the level of persistent surveillance needed for high probability of crossing activity detection and alertment of personnel for interdiction and apprehension. This presentation describes the technical capabilities current unattended ground sensors provide to support border monitoring applications. Target coverage, target classification and identity, real time reporting, and distributed information access are necessary.

To be effective an Unattended Ground Sensor System must support a mix of sensors that can offer a broadband detection capability and which can be tailored case by case on the operational and environmental requirements. Such systems will often be required to support Seismic, Acoustic, Magnetic, Passive Infra-Red (PIR), Visual Band and Infrared sensors and must be able to seamlessly transfer information to an observer to provide alerts with a low false alarm rate and, in the case of images, positive identification or intent analysis. SELEX GALILEO have developed a common processing core for its HYDRA System which delivers the key capabilities of sensor management, image compression, tracking and classification algorithms, ad-hoc wireless communication and geo-location.

This paper describes two ocean energy harvesting approaches and technologies for providing sustainable power for distributed unattended sensor and unmanned underwater vehicle networks in open ocean and in coastal and riverine areas. Technologies and systems described include energy harvesting using bottom mounted microbial fuel cells and energy harvesting from naturally occurring methane and methane hydrate deposits. The potential continuous power that could be extracted using these methods ranges from milliwatts for very small microbial fuel cells to tens of kilowatts for methane hydrate processing systems. Exploiting the appropriate naturally occurring ocean or coastal energy source will enable the placement and use of large networks of unattended sensors, both fixed in position and on rechargeable unmanned undersea vehicles. The continuous operation of such systems will have a profound impact on our knowledge of marine biological, physical and chemical processes and systems and will also facilitate improved homeland security and port surveillance.

Most designs for acoustic sensors perform some relatively simple level-based detection operation before applying more intensive resources to track the evolution of the target signature. This approach, followed in order to maintain false alarms at an acceptable level, results in the loss of information that could be derived while the signal is at levels below a set detection threshold and can result in missed or late opportunities for the all-important imaging sensors. The track-before-detect approach exploits the use of considering multiple data association alternatives forming potential tracks at low signal to noise ratios that get filtered on the basis of track dynamics to maintain an acceptable level of false alarms. This approach preserves the information derived in the early stages of track formation leading to a more complete exploitation of the available signal and result in earlier maturation of the track.

We demonstrate a cell based detection system that could be used for monitoring an underwater target volume and environment using a microfluidic chip and charge-coupled-device (CCD). This technique allows us to capture specific cells and enumerate these cells on a large area on a microchip. The microfluidic chip and a lens-less imaging platform were then merged to monitor cell populations and morphologies as a system that may find use in distributed sensor networks. The chip, featuring surface chemistry and automatic cell imaging, was fabricated from a cover glass slide, double sided adhesive film and a transparent Polymethlymetacrylate (PMMA) slab. The optically clear chip allows detecting cells with a CCD sensor. These chips were fabricated with a laser cutter without the use of photolithography. We utilized CD4⁺ cells that are captured on the floor of a microfluidic chip due to the ability to address specific target cells using antibody-antigen binding. Captured CD4⁺ cells were imaged with a fluorescence microscope to verify the chip specificity and efficiency. We achieved 70.2 ± 6.5% capturing efficiency and 88.8 ± 5.4% specificity for CD4⁺ T lymphocytes (n = 9 devices). Bright field images of the captured cells in the 24 mm × 4 mm × 50 μm microfluidic chip were obtained with the CCD sensor in one second. We achieved an inexpensive system that rapidly captures cells and images them using a lens-less CCD system. This microfluidic device can be modified for use in single cell detection utilizing a cheap light-emitting diode (LED) chip instead of a wide range CCD system.

Autonomous underwater sensors are the best solution for continuous detection of chemical species in aquatic systems. The Spectrophotometric Elemental Analysis System (SEAS), an in situ instrument that incorporates both fluorescence and colorimetric techniques, provides high-resolution time-series measurements of a wide variety of analytes. The use of Teflon AF2400 long-pathlength optical cells allows for sub-parts-per-billion detection limits. User-defined sampling frequencies up to 1 Hz facilitate measurements of chemical concentrations on highly resolved temporal and spatial scales. Due to its modular construction, SEAS can be adapted for operation in littoral or open ocean regions. We present a high-level overview of the instrument's design along with data from moored deployments and deep water casts.

In this paper a time domain waveform measurement system with active harmonic load-pull has been used to enhance the design cycle of RF power amplifiers (PAs). Wave-shaping (waveform engineering) techniques using Cardiff University's high power waveform measurement system have yielded optimum device conditions enabling a rapid PA realisation with a first-pass success. The resulting inverse class-F design, based on a 10W GaN HEMT device, is operating at 0.9GHz, and achieving 81.5% drain efficiency in both the load-pull emulated state and also in the directly realised PA. The value of measured waveforms, and the ability to engineer optimum waveforms to a specific amplifier mode, is demonstrated in this study.

The methods of detection and identification of objects based on acoustic signal analysis are used in many applications, e.g., alarm systems, military battlefield reconnaissance systems, intelligent ammunition, and others. The construction of technical objects such as vehicle or helicopter gives some possibilities to identify them on the basis of acoustic signals generated by those objects. In this paper a method of automatic detection, classification and identification of military vehicles and helicopters using a digital analysis of acoustic signals is presented. The method offers a relatively high probability of object detection in attendance of other disturbing acoustic signals. Moreover, it provides low probability of false classification and identification of object. The application of this method to acoustic sensor for the anti-helicopter mine is also presented.

CCTV operators are able to detect firearms, via CCTV, but their capacity for surveillance is limited. Thus, it is desirable to automate the monitoring of CCTV cameras for firearms using machine vision techniques. The abilities of CCTV operators to detect concealed and unconcealed firearms in CCTV footage were quantified within a signal detection framework. Additionally, the visual search strategies adopted by the CCTV operators were elicited and their efficacies indexed with respect to signal detection performance, separately for concealed and unconcealed firearms. Future work will automate effective, human visual search strategies using image processing algorithms.

This paper considers the problem of controlling a group of air vehicles with imaging sensors in order to search a region for a ground object. This can be formulated as a state-space planning problem where the states represent the possible future location and orientation of the air vehicle and resulting footprint seen by the sensor. Previous approaches have identified the mathematically optimal solution to the problem. However the planning tree within which the optimal solution is found grows very rapidly. For large search areas missions have long duration and it is infeasible, in terms of computation time, to consider all branches of the planning tree. Because of this a common approach is to constrain the solution to solving a sub problem, where plans are only created to a planning horizon. This approach can also be challenging in terms of computation time. This is dependent upon the planning horizon required to give good performance and the amount of co-operation required. Therefore many previous approaches have proposed solving sub-problems of this type sub-optimally. This paper presents an algorithm that utilises a best first search, an optimistic node selection technique, and a novel processing framework. This is then applied to optimally solve a specified sub-problem. The results demonstrate the feasibility of the approach by presenting typical computation times for various planning horizons.

Controlling mobile robots remotely requires the operator to monitor the status of the robot through some sort of feedback. Assuming a vision based feedback system is used the operator is required to closely monitor the images while navigating the robot in real time. This will engage the eyes and the hands of the operator. Since the eyes are engaged in the monitoring task anyway, their gaze can be used to navigate the robot in order to free the hands of the operator. However, the challenge here lies in developing an interaction interface that enables an intuitive distinction to be made between monitoring and commanding. This paper presents a novel means of constructing a user interface to meet this challenge. A range of solutions are constructed by augmenting the visual feedback with command regions to investigate the extent to which a user can intuitively control the robot. An experimental platform comprising a mobile robot together with cameras and eye-gaze system is constructed. The design of the system allows control of the robot, control of onboard cameras and control of the interface through eye-gaze. A number of tasks are designed to evaluate the proposed solutions. This paper presents the design considerations and the results of the evaluation. Overall it is found that the proposed solutions provide effective means of successfully navigating the robot for a range of tasks.

A system has been developed to enable a robot vehicle to autonomously explore and map an indoor environment using only visual sensors. The vehicle is equipped with a single camera, whose output is wirelessly transmitted to an off-board standard PC for processing. Visual features within the camera imagery are extracted and tracked, and their 3D positions are calculated using a Structure from Motion algorithm. As the vehicle travels, obstacles in its surroundings are identified and a map of the explored region is generated. This paper discusses suitable criteria for assessing the performance of the system by computer-based simulation and practical experiments with a real vehicle. Performance measures identified include the positional accuracy of the 3D map and the vehicle's location, the efficiency and completeness of the exploration and the system reliability. Selected results are presented and the effect of key system parameters and algorithms on performance is assessed. This work was funded by the Systems Engineering for Autonomous Systems (SEAS) Defence Technology Centre established by the UK Ministry of Defence.

Unmanned intelligent ground vehicles play significant role in wide range of applications. They are of great significance in military applications as well as other commercial applications. In order to assure the performance of unmanned intelligent vehicles, it is important to predict the reliability of the system. Reliability can be calculated using different approaches as seen in the literature, but we propose a Graph theoretic approach supported by Fuzzy and Neuro-Fuzzy approaches for predicting the node and branch reliability of the system. We portray the convoy of unmanned vehicles as a communication network where the nodes represent the station of the convoy of unmanned intelligent vehicles and the branches would represent the path between two stations. The node and branch reliability is calculated using the Fuzzy and Neuro Fuzzy approaches. The terminal and system reliability would be calculated using Boolean algebra. Thus the overall system reliability of a convoy of vehicles is the result of Fuzzy, Neuro-Fuzzy and Boolean approaches. A spanning tree based algorithm is proposed for computation of the system reliability of a convoy of vehicles. We also propose to simulate the overall system reliability with some existing data of factors that contribute in computation of node and branch reliability.

The local path planner implemented on Cornell's 2007 DARPA Urban Challenge entry vehicle Skynet utilizes a novel mixture of discrete and continuous path planning steps to facilitate a safe, smooth, and human-like driving behavior. The planner first solves for a feasible path through the local obstacle map using a grid based search algorithm. The resulting path is then refined using a cost-based nonlinear optimization routine with both hard and soft constraints. The behavior of this optimization is influenced by tunable weighting parameters which govern the relative cost contributions assigned to different path characteristics. This paper studies the sensitivity of the vehicle's performance to these path planner weighting parameters using a data driven simulation based on logged data from the National Qualifying Event. The performance of the path planner in both the National Qualifying Event and in the Urban Challenge is also presented and analyzed.

Across many consumer product industries, the prevailing practice is to design families of product variants that exploit commonality to provide the ability to easily customize a base platform for particular uses and to take advantage of commonality for streamlining design, manufacturing, maintenance and logistic; examples include Black & Decker, Seagate, and Volkswagen. This paper describes the application of product family concepts to the design and development of a family of robots to satisfy requirements for explosive ordnance disposal. To facilitate this process, we have developed a market segmentation grid that plots the desired capabilities and cost versus the target use cases. The product family design trade space is presented using a multi-dimensional trade space visualization tool which helps identify dependencies between different design variables and identify Pareto frontiers along which optimal design choices will lie. The EOD robot product family designs share common components and subsystems yet are modularized and scalable to provide functionality to satisfy a range of user requirements. This approach has been shown to significantly reduce development time and costs, manufacturing costs, maintenance and spare parts inventory, and operator and maintainer training.

A number of research workers have applied intelligent approaches for robotic applications. In the recent literature there is an increasing role of fuzzy and Neuro fuzzy approaches for unmanned vehicles. Both these approaches are based on intelligent rules. However for these applications the rules become very large and so computational time is very high. It is important to explore the approaches so as to reduce the computation time. In this paper a combination of factor analysis and clustering approaches is suggested so as to reduce the number of rules. The factor analysis can be used to reduce the number of parameters while clustering approach can be used to reduce the number of observations. Based on this methodology a new algorithm is developed which reduces the original parameters and observations into a set of new data. An algorithm is proposed and applied on a real robotic data available in a previous paper. Some of the applications for future work are proposed.

Lattice wireless sensor network (WSN) resulting from deterministic deployment by using Autonomous Unmanned Ground Vehicle (UGV) can provide optimal network QoS. It also leads insight to random WSN design by providing upper bounds for performance evaluation. In this paper, we examine the features of lattice WSNs from different perspectives by investigating square, triangular, and hexagonal pattern-based WSNs. First, we examine their node deployment efficiency in terms of the number of required sensors for achieving application-specific QoS. Then, we compare their energy efficiency by exploring the ways on grouping adjacent sensors into clusters through k-clustering and balanced clustering techniques. At last, we introduce a collision-free TDMA-based node scheduling scheme for in-cluster data aggregation in each lattice WSN. This work can be regarded as a guideline to direct the design and deployment of lattice WSNs.