| Summary: | Multiple target tracking concerns the estimation of an unknown and time-varying number of objects (targets) as they dynamically evolve over time from a sequence of measurements obtained from sensors at discrete time intervals. In the Bayesian filtering framework the estimation problem incorporates natural phenomena such as false measurements and target birth/death. Though theoretically optimal, the generally intractable Bayesian filter requires suitable approximations. This thesis is particularly motivated by a first-order moment approximation known as the Probability Hypothesis Density (PHD) filter. The emphasis in this thesis is on the further development of the PHD filter for handling more advanced target tracking problems, principally involving multiple group and extended targets. A group target is regarded as a collection of targets that share a common motion or characteristic, while an extended target is regarded as a target that potentially generates multiple measurements. The main contributions are the derivations of the PHD filter for multiple group and extended target tracking problems and their subsequent closed-form solutions. The proposed algorithms are applied in simulated scenarios and their estimate results demonstrate that accurate tracking performance is attainable for certain group/extended target tracking problems. The performance is further analysed with the use of suitable metrics.
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