Modern radar systems are required to perform a multitude of functions including highly accurate detection, parameter measurement, classification and tracking of targets over long distances. These targets may have low effective visibility and exist in a hostile environment of noise and interferences. Significant improvements in traditional monostatic radar require brute-force approaches such as larger antennas and power amplifiers, which are impractical and expensive. Multistatic radar, comprising a system of multiple, spatially separated transmitters and receivers, is one promising solution to this problem. This thesis concerns the design, development and construction of such a radar at low cost, in particular where each dispersed component of the system is mutually coherent and networked to allow cooperative operation and the joint processing of all received signals. The statistical theory of multistatic detection is analysed and processing algorithms are developed for implementation in the system. Models for the predicted coverage of the radar are developed, and illustrations of the system instrument function axe presented based on the derivation of the ambiguity function for a range of topologies and modes of operation. The requirements for obtaining spatial coherency across the system are considered, and methods of fulfilling these requirements at low cost are devised. A complete design strategy for the radar is developed, based on the use of commercial components and open architecture interfaces. The development of each major subsystem is explained, and the construction of the multistatic radar completed. Finally, the system is tested and calibrated, and some initial experiments are performed in order to determine its performance and demonstrate the advantages of this type of radar.

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The inadequacy of gaussian statistics in describing certain regions of a synthetic aperture radar (SAR) image can be explained by the violation of fundamental gaussian assumptions due to the increase in spatial resolution and target heterogeneity. Many non-gaussian probability models, competing in modeling flexibility, mathematical tractability, and simplicity / accuracy of parameter estimation, have been proposed in the last two decades to model single-channel and polarimetric SAR (PoISAR) data. This thesis explores the flexible polarimetric G distribution, which has many other nongaussian probability models as its special forms. Previously, it has not been applied to PolSAR data primarily because of its relatively complicated probability density function (pdf). But recently, other flexible distributions, e.g. Kummer-U distribution, with similarly complicated pdfs have been successfully applied to PolSAR data. Therefore, it is expected that the application of G distribution) along with the proposal of its new, accurate) and efficient parameter estimators) to model PolSAR data will bring significant contributions to the field. Firstly, singlelook version of polarimetric G distribution is derived. Then) several new parameter estimators for this distribution are proposed. The performance of these estimators are compared to each other on simulated PolSAR data. One of the better performing estimators results from the novel analysis of G distribution using Mellin kind statistics. However, this estimator does not have closed form expressions, which is an undesirable property. A new framework for parameter estimation, based on fractional moments of multilook polarimetric whitening filter, is thus proposed. It results in simple, accurate, and computationally inexpensive estimators for all the well known non-gaussian probability models including the 9 distribution. On real PolSAR data) the fitting accuracy of 9 distribution, bundled with its new estimators, is compared with some other competitive non-gaussian models. It is found that the proposed distribution adequately fits PolSAR data significantly better than its special cases, and very similar to the Kummer-U distribution. However, the software implementation of 9 distribution pdf is observed to be relatively more stable than the Kummer-U distribution pdf.

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Multitarget tracking is the process of jointly determining the number of targets present and their states from noisy sets of measurements. The difficulty of the multitarget tracking problem is that the number of targets present can change as targets appear and disappear while the sets of measurements may contain false alarms and measurements of true targets may be missed. The theory of random finite sets was proposed as a systematic, Bayesian approach to solving the multitarget tracking problem. The conceptual solution is given by Bayes filtering for the probability distribution of the set of target states, conditioned on the sets of measurements received, known as the multitarget Bayes filter. A first-moment approximation to this filter, the probability hypothesis density (PHD) filter, provides a more computationally practical, but theoretically sound, solution. The central thesis of this work is that the random finite set framework is theoretically sound, compatible with the Bayesian methodology and amenable to immediate implementation in a wide range of contexts. In advancing this thesis, new links between the PHD filter and existing Bayesian approaches for manoeuvre handling and incorporation of target amplitude information are presented. A new multitarget metric which permits incorporation of target confidence information is derived and new algorithms are developed which facilitate sequential Monte Carlo implementations of the PHD filter. Several applications of the PHD filter are presented, with a focus on applications for tracking in sonar data. Good results are presented for implementations on real active and passive sonar data. The PHD filter is also deployed in order to extract bacterial trajectories from microscopic visual data in order to aid ongoing work in understanding bacterial chemotaxis. A performance comparison between the PHD filter and conventional multitarget tracking methods using simulated data is also presented, showing favourable results for the PHD filter.