The PHD Filter for Target Swarms and Its Gaussian Mixture Implementation

The swarm is used to describe a set of individual targets which are very close together in the measurement space (relative to the resolution of the sensor). In this paper, we consider to estimate the number, shape and density of target swarms in addition to their motion states. In order to deal with the uncertain number of measurement origins, we use amplitude information (AI) to construct hypotheses about the number of individual target source of the merged measurement in the probability hypothesis density (PHD) update step. On this basis, we propose the PHD filter for target swarms (S-PHD filter) and develop Gaussian mixture (GM) implementation to the resulting filter. The performance of this algorithm is demonstrated by a multi swarms scenario with merged measurements, and the results highlight the significant improvement in the estimates of the number of individuals and the density of swarms.