A particle dyeing approach for track continuity for the SMC-PHD filter

This paper proposes a novel particle labeling (termed as `dyeing‚) method for track continuity for the sequential Monte Carlo (SMC) implementation of the probability hypothesis density (PHD) filter. The Multi-Expected a Posterior (MEAP) estimator is employed to extract estimates that is of high accuracy and fast computing speed. In the estimate extracting process, particles are dyed by the color of the closest observation (different observations have different color) that corresponds to an estimate or clutter. The estimates of two successive scans are then associated with respect to their dyeing color interaction on the particles. Unlike the general labeling method, not all particles will be labeled to an estimate/track in the dyeing process. No modification is required to make on the PHD equation due to dyeing/MEAP. The proposed estimate association method is able to handle track initialization, termination, maintenance including track splitting and merging, based on observations of successive scans.