PHD粒子滤波中目标状态提取方法研究

唐续; 魏平; 陈欣

doi:10.3724/SP.J.1146.2009.01580

PHD粒子滤波中目标状态提取方法研究

doi: 10.3724/SP.J.1146.2009.01580

唐续^{① 魏平,},
魏平,
陈欣

计量
- 文章访问数: 3898
- HTML全文浏览量: 134
- PDF下载量: 1850
- 被引次数: 31
出版历程
- 收稿日期: 2009-12-11
- 修回日期: 2010-05-04
- 刊出日期: 2010-11-19

Extracting Targets State from Particle Approximation of the PHD

Tang Xu^{① 魏平
,},
Wei Ping,
Chen Xin

摘要

摘要: 采用概率假设密度(PHD)粒子滤波进行多目标跟踪时，各时刻的目标状态表现为大量的加权粒子，需以一定方法从该粒子近似中提取出来。该文提出一种增强的目标状态提取方法，先以k-means算法对粒子进行空间分布的聚类，再于各类中寻找粒子权的峰值位置作为目标状态的估计。仿真结果表明：由于综合利用了粒子的权值和空间分布信息，该算法具有比现有算法更小的目标状态估计误差。
- 多目标跟踪 /
- 贝叶斯滤波 /
- 粒子滤波 /
- 概率假设密度 /
- 聚类
Abstract: Probability Hypothesis Density (PHD) filter has emerged as one of powerful tools for multi-target tracking. In the Sequential Monte Carlo (SMC) implementation of it, the filters output is particle approximation of PHD, so some special algorithm is needed to extract the target states from those particles. In this paper, an improved algorithm is proposed. Firstly particles are clustered by their positions using the k-means algorithm, and then the positions with maximum of particles weight are searched and estimated in each cluster as the targets positions. Because the information of both particles weight and spatial distribution are utilized, confirmed by simulation results, the new algorithm can provide estimation of the targets states more accurately.
- Multi-target tracking /
- Bayes filtering /
- Particle filter /
- Probability Hypothesis Density (PHD) /
- Clustering

HTML全文

参考文献(1)

Mahler R. Statistical Multisource-Multitarget Information Fusion[M]. Artech House, Boston, 2007: 711-715.[2]Ba-ngu vo, Singh S, and Doucet A. Sequential monte carlo methods for multi-target filtering with random finite sets[J].IEEE Transactions on Aerospace and Electronic Systems.2005, 41(4):1224-1245[3]Tobias M and Lanterman A D. Probability hypothesis density-based multitarget tracking with bistatic range and doppler observations[J].IET, Radar, Sonar and Navigation.2005, 152(3):195-205[4]Jain A K, Murty M N, and Flynn P J. Data clustering: a review[J].ACM Computing Surveys.1999, 31(3):264-323[5]Ba-ngu vo and Wing-kin MA. The gaussian mixture probability hypothesis density filter[J].IEEE Transactions on Signal Processing.2006, 54(11):4091-4104[6]Tobias M and Lanterman A D. Techniques for birth-particle placement in the probability hypothesis density particle filter applied to passive radar[J].IET, Radar, Sonar and Navigation.2008, 2(5):351-365[7]Hoffman J and Mahler R. Multitarget miss distance via optimal assignment[J].IEEE Transactions on Systems, Man and Cybernetics-Part A.2004, 34(3):327-336[8]Mahler R. PHD filters of higher order in target number[J]. IEEE Transactions on Aerospace and Electronic Systems, 2007, 43(4): 1523-1543.[9]Clark D, Ristic B, and Ba-ngu Vo. PHD Filtering with target amplitude feature[C]. 11th International Conference on Information Fusion. Cologne, Germany, Jun. 30-July 3, 2008: 1-7.[10]Streit R L. PHD intensity filtering is one step of a MAP estimation algorithm for positron emission tomography[C]. Proc of the International Conference on Information Fusion, Seattle, WA, July 6-9, 2009: 308-315.

施引文献