全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

基于容积原则的概率假设密度滤波算法

Keywords: 多目标跟踪,随机有限集,概率假设密度,容积原则,粒子滤波

Full-Text   Cite this paper   Add to My Lib

Abstract:

为改善多目标跟踪问题中概率假设密度滤波精度与算法运行时间之间的关系,提高目标状态和数目的实时估计性能,提出了基于容积原则的概率假设密度滤波算法.该算法在高斯混合粒子概率假设密度的框架下,利用容积数值积分原则直接计算非线性随机函数的均值和方差,产生粒子滤波算法的重要性函数,实现高精度粒子的重构,来近似目标状态和数目的概率分布,并且在高斯混合概率假设密度滤波算法中进行采样和更新.仿真验证了所提出算法的有效性,其Wasserstein误差距离优化了17.32%,目标数估计均值也提高了23.72%.

References

[1]  Matheron G. Random sets and integral geometry[M]. New York: Wiley, 1975.
[2]  Mahler R. A theoretical foundation for the Stein-Winter probability hypothesis density (PHD) multi-target tracking approach[C]//Proceedings of the MSS National Symposium on Sensor and Data Fusion. San Antonio, USA: [s.n.], 2000:99-117.
[3]  Sidenbladh H. Multi-target particle filtering for the probability hypothesis density[C]//Proceedings of the International Conference on Information Fusion. Cairns, Australia:[s.n.], 2003:800-806.
[4]  Vo B N, Singh S, Doucet A. Sequential Monte Carlo methods for multi-target fltering with random finite sets[J]. IEEE Transactions on Aerospace and Electronic Systems, 2005,41(4):1224-1245.
[5]  Vo Ba-Ngu, Ma Wing-Kin. The Gaussian mixture probability hypothesis density filter[J]. IEEE Trans Signal Processing, 2006,54(11):4091-4104.
[6]  张俊根,姬红兵.高斯混合粒子PHD滤波被动测角多目标跟踪[J]. 控制与决策,2011,26(3):413-417. Zhang Jungen, Ji Hongbing. Gaussian mixture particle probability hypothesis density based passive bearings-only multi-target tracking[J]. Control and Decision, 2011,26(3):413-417. (in Chinese)
[7]  吕学斌,游志胜,周群彪,等.基于无迹变换的概率假设密度滤波算法[J].系统仿真学报,2009,21(3):845-850. Lü Xuebin, You Zhisheng, Zhou Qunbiao, et al. Probability hypothesis density filter based on unscented transformation and its application to multi-target tracking[J]. Journal of System Simulation, 2009,21(3):845-850. (in Chinese)
[8]  黄伟平,徐毓,甘少武.抗"飞点"的UKF-GMPCPHD滤波算法[J].系统工程与电子技术,2012,34(1):34-39. Huang Weiping, Xu Yu, Gan Shaowu. New UKF-GMPCPHD algorithm for outliers\'rejection[J]. Systems Engineering and Electronics, 2012,34(1):34-39.(in Chinese)
[9]  Whiteley N, Singh S, Godsill S. Auxiliary particle implementation of probability hypothesis density filter[J]. IEEE Transaction on Aerospace and Electronic Systems, 2010,46(3):1437-1454.
[10]  Nadarajah N, Kirubarajan T, Lang T. Multitarget tracking using probability hypothesis density smoothing[J]. IEEE Transaction on Aerospace and Electronic Systems, 2011,47(4):2344-2360.
[11]  Mahler R P S, Vo B T, Vo B N. Forward-backward probability hypothesis density smoothing[J]. IEEE Transaction on Aerospace and Electronic Systems, 2012,48(1):7-28.
[12]  Arasaratnam I, Haykin S. Cubature Kalman filters[J]. IEEE Trans on Automatic Conrol, 2009,54(6):1254-1269.
[13]  Arasaratnam I, Haykin S, Hurd T R. Cubature Kalman filtering for continuous-discrete systems: theory and simulations[J]. IEEE Trans on Signal Processing, 2010,58(10):4977-4993.
[14]  Clark D, Vo B T, Vo B N. Gaussian particle implementations of probability hypothesis density filters[C]//Proceedings of 2007 IEEE Aerospace Conference. Big Sky, MT:[s.n.], 2007:1-11.
[15]  Kotecha J, Djuric P. Gaussian particle filtering[J]. IEEE Transactions on Signal Processing, 2003,51(10):2592-2601.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133