A novel fast partitioning algorithm for extended target tracking using a Gaussian mixture PHD filter

Abstract

In an extended target PHD filter, the exact filter requires all possible partitions of the current measurement set for updating, which is computationally intractable. In order to limit the number of partitions, a fast partitioning algorithm for extended target Gaussian mixture PHD (ET-GM-PHD) filter is proposed, which substitutes Distance Partitioning with a fuzzy ART model. Alternative partitions of the measurement set are generated by the different vigilance values in ART. Suitable measures and remedies are given to handle the problems arisen by overestimation of target number and spatially close targets. The simulation results show that the proposed algorithm can well handle the close-spaced targets and obviously reduce computational burden without losing tracking performance, which implies good application prospects for the real-time extended target tracking system.