Target maneuver detection using a particle filter with spawn model and particle labeling

This paper presents a novel single target particle filter with spawn model and particle labeling approach, abbreviated SL-PF. The purpose of this filter is to detect instantaneously occurring target maneuvers, e.g. course changes of maritime targets, and to provide accurate tracking performance before and after the maneuvers. The key idea is to borrow the spawn model from the probability hypothesis density (PHD) filter since this model is naturally suited for these kinds of maneuvers. Secondly, each particle in the filter carries a label which is updated in a systematic manner in the spawning step so that it is possible to recognize spawned particles representing a target maneuver. This provides an integrated maneuver detection procedure within the particle filter. Monte Carlo simulations verify the SL-PF approach and indicate a significant estimation accuracy improvement compared to a conventional particle filter.