Improvement and application of Gaussian mixture probability hypothesis density filter

被引：0
|
作者：
Cang Y. [1 ]
Ma Y. [1 ]
Qiao Y.-L. [1 ]
机构：
[1] College of Information and Communication Engineering, Harbin Engineering University, Harbin
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2016年 / 38卷 / 11期
关键词：
Frequency estimation; Multiple target; Probability hypothesis density; Tracking algorithm;
D O I：
10.3969/j.issn.1001-506X.2016.11.05
中图分类号：
学科分类号：
摘要：
In Gaussian mixture probability hypothesis density (GM-PHD) filter, the new born targets exist the whole detection domain, and the specific position is hard to define for occurring randomly. Therefore, an improve algorithm, using the received measurements adaptively generate target intensity function to record the surviving target intensity function, is realized. The algorithm can adaptively distinguish the surviving and new born target intensity function to improve the accuracy. Multiple target position tracking simulation and the dolphin whistle real data processing are used to test improved algorithm performance, and optional sub pattern assignment (OSPA) function works as a benchmark. The result shows that the proposed algorithm improves the target number estimation and tracking accuracy. The correct rate of target number estimation is 97%, and the OSPA distance is decreased nearly 30% of the original GM-PHD filter. © 2016, Editorial Office of Systems Engineering and Electronics. All right reserved.
引用
收藏
页码：2481 / 2486
页数：5
相关论文
共 14 条
  • [1] Nait-Charif H., Mckenna S.J., Head tracking and action recognition in a smart meeting room, IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, 23, 3, pp. 24-31, (2003)
  • [2] Coifman B., Beymer D., Mclauchlan P., Et al., A real-time computer vision system for vehicle tracking and traffic surveillance, Transportation Research Part C: Emerging Technologies, 6, 4, pp. 271-288, (1998)
  • [3] Serby D., Kikker-Meier S., Gool L.V., Probabilistic object tracking using multiple features, IEEE International Conference of Pattern Recognition, 34, 13, pp. 184-187, (2004)
  • [4] Maher R., Multi-target moments and their application to multi-target tracking, Proc. of the Workshop on Estimation, Tracking and Fusion, pp. 134-166, (2001)
  • [5] Mahler R.P.S., Multi-target Bayes filtering via first-order multi-target moments, IEEE Trans. on Aerospace and Electronic Systems, 39, 4, pp. 1152-1178, (2003)
  • [6] Bar-Shalom Y., Li X., Multitarget multisensor tracking, Principles and Techniques, 56, 7, pp. 125-145, (1995)
  • [7] Dai D.Z., The study and applictaion of particle filter algorithm on target tracking, 6, 3, pp. 1-67, (2006)
  • [8] Vo B., Ma W.K., The Gaussian mixture probability hypothesis density filter, IEEE Trans. on Signal Process, 54, 11, pp. 4091-4104, (2006)
  • [9] Yang F., Wang Y.Q., Liang Y., Et al., The summarize of multi-target tracking based on probability hypothesis density filter method, Acta Automatica Sinica, 39, 11, pp. 1945-1952, (2013)
  • [10] Beard M., Vo B.T., Vo B.N., Et al., A partially uniform target birth model for Gaussian mixture PHD/CPHD filtering, IEEE Trans. on Aerospace and Electronic Systems, 49, 4, pp. 2835-2844, (2013)
12