Robust Bayesian partition for extended target Gaussian inverse Wishart PHD filter

Abstract

Extended target Gaussian inverse Wishart PHD filter is a promising filter. However, when the two or more different sized extended targets are spatially close, the simulation results conducted by Granstrom et al. show that the cardinality estimate is much smaller than the true value for the separating tracks. In this study, the present authors call this phenomenon as the cardinality underestimation problem, which can be solved via a novel robust clustering algorithm, called Bayesian partition, derived by combining the fuzzy adaptive resonance theory with Bayesian theorem. In Bayesian partition, alternative partitions of the measurement set are generated by the different vigilance parameters. Simulation results show that the proposed partitioning method has better tracking performance than that presented by Granstrom et al., implying good application prospects.