This thesis focuses upon automatic target recognition (ATR) with radar sensors. Recent advancements in ATR have included the processing of target signatures from multiple, spatially-diverse perspectives. The advantage of multiple perspectives in target classification results from the angular sensitivity of reflected radar transmissions. By viewing the target at different angles, the classifier has a better opportunity to distinguish between target classes. This dissertation extends recent advances in multi-perspective target classification by: 1) leveraging bistatic target reflectivity signatures observed from multiple, spatially-diverse radar sensors; and, 2) employing a statistical distance measure to identify radar sensor locations yielding improved classification rates. The algorithms provided in this thesis use high resolution range (HRR) profiles – formed by each participating radar sensor – as input to a multi-sensor classification algorithm derived using the fundamentals of statistical signal processing. Improvements to target classification rates are demonstrated for multiple configurations of transmitter, receiver, and target locations. These improvements are shown to emanate from the multi-static characteristics of a target class’ range profile and not merely from non-coherent gain. The significance of dominant scatterer reflections is revealed in both classification performance and the “statistical distance” between target classes. Numerous simulations have been performed to interrogate the robustness of the derived classifier. Errors in target pose angle and the inclusion of camouflage, concealment, and deception (CCD) effects are considered in assessing the validity of the classifier. Consideration of different transmitter and receiver combinations and low signal-to-noise ratios are analyzed in the context of deterministic, Gaussian, and uniform target pose uncertainty models. Performance metrics demonstrate increases in classification rates of up to 30% for multiple-transmit, multiple-receive platform configurations when compared to multi-sensor monostatic configurations. A distance measure between probable target classes is derived using information theoretic techniques pioneered by Kullback and Leibler. The derived measure is shown to suggest radar sensor placements yielding better target classification rates. The predicted placements consider two-platform and three-platform configurations in a single-transmit, multiple-receive environment. Significant improvements in classification rates are observed when compared to ad-hoc sensor placement. In one study, platform placements identified by the distance measure algorithm are shown to produce classification rates exceeding 98.8% of all possible platform placements.

This dissertation lays out a multi-static radar system with mobile receivers. The transmitter is at a known location emitting a radar signal that bounces off a target. The echo is received by a team of UAVs that are capable of estimating both time-delay and Doppler from the received signal. Several methods for controlling the movement of mobile sensor platforms are presented to improve target tracking performance. Two optimization criteria are derived for the problem, both of which require some type of search procedure to find the desired solution. Simulations are used to show the benefit of using closed-loop sensor control for the special case of an EKF tracking filter. In addition, a simpler closed-form approach based on one of the algorithms is also presented and is shown to have performance similar to that obtained using the optimal algorithms. To decentralize the estimation in the UAV network, an information consensus filter (ICF) is presented. In the ICF each agent maintains a local estimate, which is shown to be unbiased and conservative with respect to the local covariance matrix estimate. The ICF does not take into account unknown track-to-track correlation that occurs when local independent estimates pass through a common process model. However, it does eliminate the redundancy incurred when communicating information through general network topologies, including graphs containing loops. In the ICF a discrete-time consensus filter is used to handle the communication of information between nodes (UAVs) in the network. Communication is local in that each agent can only communicate with local neighbors and not the entire network. A second-order discrete-time consensus protocol is developed. Necessary and sufficient conditions are given that ensure the team of agents achieves consensus using the second-order protocol. Using insights from the analysis of the ICF an extension is made by adding an observation buffer to the ICF. The new filter is called the information consensus filter with an observation buffer (ICFOB). The track-to-track correlation occurring from independent estimates passing through a common process model does not affect the ICFOB as it does other decentralized estimation methods. The ICFOB is shown to be equivalent to a centralized filter that has access to every measurement in a network. There are two caveats to this equivalency. First, at any point in time, the prior ICFOB estimate is equal to the prior centralized filter estimate found by fusing the observations that are taken before those stored in the buffer. The a posteriori estimates using observations in the buffer are not equal to estimates from the centralized filter since the agents have not finished disseminating those observations throughout the sensor network. Second, the ICFOB needs to know the number of active sensors in the network. The number of sensors is global information; therefore, the ICFOB is not fully decentralized. If the number of sensors is not known, the local estimates are conservative.