Sequential Monte Carlo methods for extended and group object tracking

This dissertation deals with the challenging tasks of real-time extended and group object tracking. The problems are formulated as joint parameter and state estimation of dynamic systems. The solutions proposed are formulated within a general nonlinear framework and are based on the Sequential Monte Carlo (SMC) method, also known as Particle Filtering (PF) method. Eour different solutions are proposed for the extended object tracking problem. The first two are based on border parametrisation of the visible surface of the extended object. The likelihood functions are derived for two different scenarios - one without clutter in the measurements and another one in the presence of clutter. In the third approach the kernel density estimation technique is utilised to approximate the joint posterior density of the target dynamic state and static size parameters. The forth proposed approach solves the extended object tracking problem based on the recently emerged SMC method combined with interval analysis , called Box Particle Filter (Box P F). Simulation results for all of the developed algorithms show accurate online tracking, with very good estimates both for the target kinematic states and for the parameters of the target extent. In addition, the performance of the Box PF and the border parametrised PF is validated utilising real measurements from laser range scanners obtained within a prototype security system replicating an airport corridor.