An improved partitioning algorithm based on FCM algorithm for extended target tracking in PHD filter

Abstract

Extended target Gaussian mixture probability hypothesis density (ET-GM-PHD) filter is a promising approach for multi-extended target tracking problem. Correct partitions of measurements are significant to the GM-PHD filter. In order to partition the measurement sets accurately and rapidly, an improved Fuzzy C-Means (FCM) approach is proposed. The proposed approach consists of two stages. Firstly, elliptical gating and Gaussian kernel density analysis technique are used to remove the clutter. Secondly, an improved FCM approach which takes the full merits of the prior information of targets is implemented. The predictive state of targets is utilized in the FCM initialization. And the shape of targets is considered in the iteration of FCM. Both real data and simulated data are performed in this work. Experimentation infers that the proposed method outperforms the others on both effectiveness and computation, especially when multiple closely targets are involved.