Efficient update of persistent particles in the SMC-PHD filter

The paper is devoted to the implementation of the Sequential Monte Carlo Probability Hypothesis Density (SMC-PHD) filter. A measurement driven proposal for persistent target particles requires the predicted persistent target particles to be partitioned in a probabilistic manner using the received measurement set. Each partition is subsequently updated using a conveniently designed efficient proposal distribution (in this paper we apply the progressive correction). The performance of the described algorithm is demonstrated in the context of autonomous tracking of multiple moving targets using bearings-only measurements.