Improved Gaussian Mixture CPHD Tracker for Multitarget Tracking

Abstract

The Gaussian mixture cardinality probability hypothesis density (GM-CPHD) tracker is a promising algorithm for multitarget tracking. However, there are two major problems with it. First, when missed detections occur, the probability hypothesis density (PHD) weight will be shifted from the undetected part to the detected part, no matter how far apart the parts are. Second, when targets are close to or cross each other, the GM-CPHD tracker may fail to discriminate different tracks because the score of each track hypothesis in the traditional method is updated by simply summing the log likelihood ratios (LLR) between successive scans. To solve these problems an improved GM-CPHD tracker is proposed that minimizes the effect of the weight shifting and subsequent estimation errors by a dynamic reweighting scheme and improves the performance of track continuity by a dynamic track management scheme. Simulation results show that the improved GM-CPHD tracker is superior to the traditional methods in both the aspects of target state estimate and maintenance of track continuity so that this improved GM-CPHD tracker will have good application prospects.