MULTIPLE TARGET TRAJECTORY CONTINUITY BASED ON GAUSSIAN MIXTURE PROBABILITY HYPOTHESIS DENSITY

The probability hypothesis density (PHD) filter is a promising tool for tracking the time-varying number of targets in real time. Gaussian mixture is an approximation scheme to obtain the closed solution of the PHD filter. By using Gaussian component labels in the GM-PHD filter, the identities of individual target can be obtained. However, the labeling GM-PHD filter cannot correctly discriminate tracks of individual targets when targets move closely to each other. To solve this problem, a novel multiple target trajectory continuity algorithm based on the GM-PHD filter is proposed, which is able to identify and manage the track of each individual target effectively. First, a detection-guided dynamic reweight scheme is employed in the GM-PHD filter to alleviate the weight error of closely spaced targets. Then, a novel trajectory continuity scheme is introduced to form and maintain the tracks of individual targets. Simulation results demonstrate that the proposed approach can achieve better performance compared to the labeling GM-PHD filter.