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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.

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In high range-resolution (HRR) radar systems, the returns from a single target may fall in multiple adjacent range bins which individually vary in amplitude. A target following this representation is commonly referred to as an extended target and results in more information about the target. However, extracting this information from the radar returns is challenging due to several complexities. These complexities include the single dimensional nature of the radar measurements, complexities associated with the scattering of electromagnetic waves, and complex environments in which radar systems are required to operate. There are several applications of HRR radar systems which extract target information with varying levels of success. A commonly used application is that of imaging referred to as synthetic aperture radar (SAR) and inverse SAR (ISAR) imaging. These techniques combine multiple single dimension measurements in order to obtain a single two dimensional image. These techniques rely on rotational motion between the target and the radar occurring during the collection of the single dimension measurements. In the case of ISAR, the radar is stationary while motion is induced by the target. There are several difficulties associated with the unknown motion of the target when standard Doppler processing techniques are used to synthesise ISAR images. In this dissertation, a non-standard Dop-pler approach, based on Bayesian inference techniques, was considered to address the difficulties. The target and observations were modelled with a non-linear state space model. Several different Bayesian techniques were implemented to infer the hidden states of the model, which coincide with the unknown characteristics of the target. A simulation platform was designed in order to analyse the performance of the implemented techniques. The implemented techniques were capable of successfully tracking a randomly generated target in a controlled environment. The influence of varying several parameters, related to the characteristics of the target and the implemented techniques, was explored. Finally, a comparison was made between standard Doppler processing and the Bayesian methods proposed. / Dissertation (MEng)--University of Pretoria, 2013. / gm2014 / Electrical, Electronic and Computer Engineering / unrestricted

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