论文信息 - Data association for PHD filter based on MHT

Data association for PHD filter based on MHT

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.

[1] Ronald P. S. Mahler,et al. Multitarget miss distance via optimal assignment , 2004, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[2] K. S. P. Kumar,et al. A 'current' statistical model and adaptive algorithm for estimating maneuvering targets , 1984 .

[3] Thiagalingam Kirubarajan,et al. Data association combined with the probability hypothesis density filter for multitarget tracking , 2004, SPIE Defense + Commercial Sensing.

[4] I. R. Goodman,et al. Mathematics of Data Fusion , 1997 .

[5] Y. Bar-Shalom. Tracking and data association , 1988 .

[6] Ba-Ngu Vo,et al. Probability hypothesis density filter versus multiple hypothesis tracking , 2004, SPIE Defense + Commercial Sensing.

[7] R. Mahler. Multitarget Bayes filtering via first-order multitarget moments , 2003 .

[8] Ba-Ngu Vo,et al. The Gaussian Mixture Probability Hypothesis Density Filter , 2006, IEEE Transactions on Signal Processing.

[9] Sumeetpal S. Singh,et al. Sequential monte carlo implementation of the phd filter for multi-target tracking , 2003, Sixth International Conference of Information Fusion, 2003. Proceedings of the.