Title :
Data association for PHD filter based on MHT
Author :
Wang, Yang ; Jing, Zhongliang ; Hu, Shiqiang
Author_Institution :
Sch. of Electron., Shanghai Jiao Tong Univ., Shanghai
fDate :
June 30 2008-July 3 2008
Abstract :
The main drawback of probability hypothesis density (PHD) filter is that it canpsilat identify the trajectories of the different targets. Data association for PHD filter based on multiple hypotheses tracking (MHT) is presented to solve the problem. The track-oriented MHT is used to perform data association on the output of PHD filter. An adaptive Kalman filter based on ldquocurrentrdquo statistic model, combined with MHT, is implemented to track maneuvering targets. Two examples are given to test the performance of the new method. Monte Carlo simulation results show that this approach is computationally feasible and effective for associating multi-targets in dense clutter environments.
Keywords :
Kalman filters; Monte Carlo methods; adaptive filters; probability; sensor fusion; statistical analysis; target tracking; Monte Carlo simulation; adaptive Kalman filter; data association; maneuvering target tracking; multiple hypotheses tracking; probability hypothesis density filter; statistic model; data association; probability hypothesis density; track-oriented MHT;
Conference_Titel :
Information Fusion, 2008 11th International Conference on
Conference_Location :
Cologne
Print_ISBN :
978-3-8007-3092-6
Electronic_ISBN :
978-3-00-024883-2
