一种基于概率密度传播的目标跟踪算法

高庆华; 金明录; 王洁; 王洪玉

doi:10.3724/SP.J.1146.2009.01404

一种基于概率密度传播的目标跟踪算法

doi: 10.3724/SP.J.1146.2009.01404

基金项目:

国家自然科学基金(60871046)资助课题

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出版历程
- 收稿日期: 2009-10-29
- 修回日期: 2010-05-25
- 刊出日期: 2010-10-19

A Tracking Algorithm Based on Probability Density Propagation

摘要

摘要: 该文提出一种解决非线性、非高斯条件下目标跟踪问题的新方法，在贝叶斯框架下通过连续概率密度传播实现目标跟踪。采用高斯混合模型表征目标先验分布、后验分布及观察似然函数，利用无迹变换实现目标位置的非线性预测，通过拟合方法获得后验分布，同时，将后验分布各模式的加权质心作为目标的位置估计。仿真结果表明，该算法可以很好地解决大噪声环境下基于无线传感器网络的目标跟踪问题。
- 无线传感器网络 /
- 跟踪 /
- 概率密度传播 /
- 贝叶斯估计
Abstract: A tracking algorithm based on probability density propagation which can deal with non-linear and non-Gaussian issues is proposed. The Gaussian mixture model is adopted to represent the prior density distribution, posterior density distribution and likelihood distribution. The unscented transformation is used to deal with the non-linear prediction and approximation method is used to achieve the posterior density distribution. Finally, the weighted centroid point of the posterior density distributions different modes is calculated and set as the current position of the target. Simulation results indicate that the proposed algorithm can deal with the tracking task in wireless sensor network under strong noise.
- Wireless sensor network /
- Tracking /
- Probability density propagation /
- Bayesian estimation

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[1] Doucet A, Godsill S, and Andrieu C. On sequential Monte Carlo sampling methods for Bayesian filtering[J].Statistics and Computing.2000, 10(3):197-208 [2] Merwe R, Doucet A, and Freitas N, et al.. The unscented particle filter[C]. 14th Annual Neural Information Processing Systems Conference, Denver, Colorado, USA, Nov.27-Dec.2, 2000: 584-590. [3] Rui Yong and Chen Yun-qiang. Better proposal distributions: object tracking using unscented particle filter[C]. Proceeding of the Conference on Computer Vision and Pattern Recognition, Hawaii, USA, Dec.9-14, 2001: 786-793. [4] Cheng Qi and Bondon P. A new unscented particle filter[C]. International Conference on Acoustics, Speech and Signal Processing, Las Vegas, Nevada, USA, March 30-April 4, 2008: 3417-3420. [5] Kotecha J H and Djuric P M. Gaussian sum particle filtering[J].IEEE Transactions on Signal Processing.2003, 51(10):2602-2612 [6] Kotecha J H and Djuric P M. Gaussian particle filtering[J].IEEE Transactions on Signal Processing.2003, 51(10):2592-2601 [7] Luo Hai-yong, Li Jin-tao, and Zhao Fang, et al.. Mobile target localization based on mean shift in wireless sensor networks[C]. Third International Conference on Pervasive Computing and Applications, Alexandria, Egypt, October 6-8, 2008: 248-253. [8] Baggio A and Langendoen K. Monte Carlo localization for mobile wireless sensor networks[J].Ad hoc Networks.2008, 6(5):718-733 [9] Han Bohyung, Zhu Ying, and Comaniciu D, et al.. Visual tracking by continuous density propagation in sequential Bayesian filtering framework[J].IEEE Transactions on Pattern Analysis and Machine Intelligence.2009, 31(5):919-930 [10] Sheng Xiao-hong, Hu Yu-hen, and Ramanathan P. Distributed particle filter with GMM approximation for multiple targets localization and tracking in wireless sensor network[C]. Fourth International Symposium on Information Processing in Sensor Networks, Los Angeles, California, USA, April 25-27, 2005: 181-188. [11] Ding Min and Cheng Xiu-zhen. Fault tolerant target tracking in sensor networks[C]. Proceedings of the 10th ACM International Symposium on Mobile Ad hoc Networking and Computing, New Orleans, LA, USA, May 18-21, 2009: 125-134. [12] Yuan Xiang-hui, Han Chong-zhao, and Duan Zhan-sheng, et al.. Comparison and choice of models in tracking target with coordinated turn motion[C]. IEEE 8th International Conference on Information Fusion, Philadelphia, PA, USA, July 25-28, 2005, 2: 1497-1502.

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