Maneuvering Multi-target Tracking Using the Multi-model Cardinalized Probability Hypothesis Density Filter

Tracking an unknown and time-varying number of maneuvering targets is a challenging problem in the presence of noise, clutter, uncertainties in target maneuvers, data association, and detection. To account for this problem, a multi-model extension of the Cardinalized probability hypothesis density (CPHD) filter is proposed in this paper. Additionally, a particle implementation and a Gaussian mixture implementation of the proposed extension are given for generic models and linear Gaussian models, respectively. The effectiveness of the extension is illustrated through Monte Carlo simulation.