Performance of an emerging MB filter

A method for solving the multi-target tracking (MTT) problem in urban environments is presented. The difficulties specific to urban environments include changing target cardinality, high target density, and targets that present different types of motion. The solution presented involves the use of the Multi-Object Particle Multi-Bernoulli (MOP-MB) filter, a computationally efficient approximation of the Bayes' Multi-Object Filter. This filter is then extended to employ the use of multiple motion models that combine to provide a better estimate of target state and cardinality. The new filter, called the Interacting Multiple Model Multi-Object Particle Multi-Bernoulli (IMM-MOP-MB), uses the multi-object particles (MOPs) to additionally estimate the target's motion mode. We then compare the performance of these MB filters with an IMMJPDAF with track management software in terms of cardinality tracking and position estimates. This is done through the use of the Generalized Optimal Sub-Pattern Assignment (GOSPA) metric and we find that the IMM-MOP-MB performs best in all test scenarios.