Multiple Model Poisson Multi-Bernoulli Mixture Filter for Maneuvering Targets

Abstract

The Poisson multi-Bernoulli mixture (PMBM) filter is conjugate prior composed of the union of a Poisson point process (PPP) and a multi-Bernoulli mixture (MBM). In this paper, a new PMBM filter for tracking multiple targets with randomly time-varying dynamics under multiple model (MM) is considered. The proposed MM-PMBM filter uses extends the single-model PMBM filter recursion to multiple motion models by using the jump Markov system (JMS). The performance of the proposed algorithm is examined and compared with the MM-MB filter. The simulation results demonstrate that the proposed MM-PMBM filter outperforms the MM-MB filter in terms of the tracking accuracy, including the target states and cardinality, especially for the scenerio with low detection probability. Moreover, the comparisons for the variations of detection probability and standard derivation of measurement noise are also tested via simulation experiments.