改进的多模型粒子PHD和CPHD滤波算法

欧阳成; 姬红兵; 郭志强

doi:10.3724/SP.J.1004.2012.00341

改进的多模型粒子PHD和CPHD滤波算法

doi: 10.3724/SP.J.1004.2012.00341

1.
西安电子科技大学电子工程学院西安 710071

详细信息

通讯作者:
欧阳成, 西安电子科技大学电子工程学院博士研究生. 主要研究方向为数字信号处理,目标跟踪,数据融合. E-mail: ouoyc@yahoo.com.cn oindent

计量
- 文章访问数: 3199
- HTML全文浏览量: 99
- PDF下载量: 1373
- 被引次数: 0
出版历程
- 收稿日期: 2010-12-15
- 修回日期: 2011-11-20
- 刊出日期: 2012-03-20

Improved Multiple Model Particle PHD and CPHD Filters

1.
School of Electronic Engineering, Xidian University, Xi'an 710071

摘要

摘要: 多模型粒子概率假设密度(Probability hypothesis density, PHD)滤波是一种有效的多机动目标跟踪算法, 然而当模型概率过小时,该算法存在粒子退化问题,而且它对目标数的泊松分布假设会夸大目标漏检对其势估计的影响. 针对上述问题,本文提出一种改进算法. 该算法并不是简单地对模型索引进行采样, 而是用粒子拟合目标状态的模型条件PHD强度, 在不对噪声做任何先验假设的前提下, 通过重采样实现存活粒子的输入交互,提高了滤波性能. 在此基础上, 进一步将算法在Cardinalized PHD (CPHD)的框架下加以实现,提高其目标数估计精度. 仿真实验表明,所提算法在滤波性能和目标数估计精度方面均优于传统的多模型粒子PHD算法,具有良好的工程应用前景.
- 多模型 /
- 粒子滤波 /
- 概率假设密度滤波 /
- 机动目标跟踪
Abstract: The multiple model probability hypothesis density (PHD) filter is an effective algorithm for tracking multiple maneuvering targets. However, when the conditional mode probabilities have small values, there is a particle degenerate problem and the Poisson assumption for the target number distribution will lead to an exaggerating effect of missed detections on the target number estimation. To solve these problems, an improved algorithm is proposed in this paper, which approximates the model conditional probability hypothesis density of target states by particles, and makes the interaction between survival targets by resampling, without any a priori assumption of the noise. Further more, the improved algorithm is implemented in the framework of the cardinalized PHD (CPHD) filter, so as to improve the accuracy of target number estimation. The simulation results show that the improved algorithm has better performance in terms of state filtering and target number estimation, so that this algorithm will have good application prospects.
- Multiple model /
- particle filter(PF) /
- probability hypothesis density (PHD) filter /
- maneuvering target tracking

HTML全文

参考文献(1)

[1]

McGinnity S, Irwin G W. Multiple model bootstrap filter for maneuvering target tracking. IEEE Transactions on Aerospace and Electronic Systems, 2000, 36(3): 1006-1012[2] Gordon N J, Maskell S, Kirubarajan T. Efficient particle filters for joint tracking and classification. In: Proceedings of the Signal and Data Processing of Small Targets. Orlando, USA: SPIE, 2002. 439-449[3] Wang Ming-Hui, You Zhi-Sheng, Han Rong-Chun, Zhang Jian-Zhou. The advanced IMM-PDAF algorithm in a heavy clutter environment. Acta Automatica Sinica, 2001, 27(2): 267-271(王明辉, 游志胜, 韩荣椿, 张建州. 强干扰环境下性能优化的相互作用多模型--概率数据关联算法. 自动化学报, 2001, 27(2): 267-271)[4] Pollard E, Pannetier B, Rombaut M. Hybrid algorithms for multitarget tracking using MHT and GM-CPHD. IEEE Transactions on Aerospace and Electronic Systems, 2011, 47(2): 832-847[5] Mahler R. Multitarget Bayes filtering via first-order multi-target moments. IEEE Transactions on Aerospace and Electronic Systems, 2003, 39(4): 1152-1178[6] Lian Feng, Han Chong-Zhao, Liu Wei-Feng, Yuan Xiang-Hui. Tracking partly resolvable group targets using SMC-PHDF. Acta Automatica Sinica, 2010, 36(5): 731-741(连峰, 韩崇昭, 刘伟峰, 元向辉. 基于SMC-PHDF的部分可分辨的群目标跟踪算法. 自动化学报, 2010, 36(5): 731-741)[7] Vo B N, Ma W K. The Gaussian mixture probability hypothesis density filter. IEEE Transactions on Signal Processing, 2006, 54(11): 4091-4104[8] Li W, Jia Y M. Gaussian mixture PHD filter for jump Markov models based on best-fitting Gaussian approximation. Signal Processing, 2011, 91(4): 1036-1042[9] Liu W F, Han C Z, Lian F, Zhu H Y. Multitarget state extraction for the PHD filter using MCMC approach. IEEE Transactions on Aerospace and Electronic Systems, 2010, 46(2): 864-883[10] Pasha S A, Vo B N, Tuan H D, Ma W K. A Gaussian mixture PHD filter for jump Markov system models. IEEE Transactions on Aerospace and Electronic Systems, 2009, 45(3): 919-936[11] Lian Feng, Han Chong-Zhao, Liu Wei-Feng, Yuan Xiang-Hui. Multiple-model probability hypothesis density smoother. Acta Automatica Sinica, 2010, 36(7): 939-950(连峰, 韩崇昭, 刘伟峰, 元向辉. 多模型概率假设密度平滑器. 自动化学报, 2010, 36(7): 939-950)[12] Clark D, Vo B N. Convergence analysis of the Gaussian mixture PHD filter. IEEE Transactions on Signal Processing, 2007, 55(4): 1204-1212[13] Punithakumar K, Kirubarajan T, Sinha A. Multiple-model probability hypothesis density filter for tracking maneuvering targets. IEEE Transactions on Aerospace and Electronic Systems, 2008, 44(1): 87-98[14] Mahler R. PHD filters of higher order in target number. IEEE Transactions on Aerospace and Electronic Systems, 2007, 43(4): 1523-1543[15] Ouyang Cheng, Ji Hong-Bing, Zhang Jun-Gen. Improved CPHD filter for multitarget tracking. Journal of Electronics and Information Technology, 2010, 32(9): 2112-2118(欧阳成, 姬红兵, 张俊根. 一种改进的CPHD多目标跟踪算法. 电子与信息学报, 2010, 32(9): 2112-2118)[16] Blom H, Bloem E A. Exact Bayesian and particle filtering of stochastic hybrid systems. IEEE Transactions on Aerospace and Electronic Systems, 2007, 43(1): 55-70[17] Schuhmacher D, Vo B T, Vo B N. A consistent metric for performance evaluation of multi-object filters. IEEE Transactions on Signal Processing, 2008, 56(8): 3447-3457

施引文献

资源附件(0)

访问统计