An Improved Measurement-Oriented Marginal Multi-Bernoulli/Poisson Filter

Abstract

The measurement-oriented marginal multi-Ber-noulli/Poisson (MOMB/P) filter is an attractive approach for multi-target tracking. However, the effect of measure¬ment on predicted target states may be weakened when the hypothesized tracks are separated, even if the measurement is close to the predicted target state. This is due to the inaccuracy of the missed detection hypothesis probabilities in the marginal association probabilities. To solve this problem, an improved MOMB/P (IMOMB/P) filter is pro¬posed in this paper, by considering the measurement infor¬mation in the missed detection hypotheses. Simulation results reveal a favorable comparison to the MOMB/P filter in terms of the Optimal Subpattern assignment (OSPA) distance and cardinality estimation.