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Group Object Tracking with a Sequential Monte Carlo Method Based on a Parameterised Likelihood Function

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Abstract

Group objects are characterised with multiple measurements originating from
different locations of the targets constituting the group. This paper presents a novel Sequential Monte Carlo approach for tracking groups with a large number of components, applicable to various nonlinear problems. The novelty in this work is in the derivation of the likelihood function for nonlinear measurement functions, with sets of measurements belonging to a bounded spatial region. Simulation results are presented when a group of 50 objects is surrounded by a circular region. Estimation results are given for the group object center and extent.