An Improved Algorithm for Tracking Mulitiple Extended Targets Based on Measurement Set Partitioning

Abstract

In the background of clutter, the probability hypothesis density (PHD) filter is used to carry out the extended target tracking where the measurement set is difficult to partition and the computational efficiency is low. A method is proposed to divide the measurements for extended target by using the Clusters Optimization based on Density of Hierarchical Partition (CODHD) clustering algorithm. Firstly, the adaptive ellipsoid threshold method is used to pre-process the measurement set to filter ineffective clutter; then the optimal cluster result is obtained by evaluating cluster quality assessment for each partition; finally measurement partition is obtained through fuzzy C-means (FCM) operation. The simulation results have shown that the method can be used to divide the measurement set while the good performance of the extended target filter can be obtained, and the cost of the calculation is reduced.