期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

高斯混合粒子PHD滤波被动测角多目标跟踪 被引量：12

Gaussian mixture particle probability hypothesis density based passive bearings-only multi-target tracking
原文传递
导出
摘要 为解决目标数未知或随时间变化的多目标跟踪问题,通常将多目标状态和观测数据表示成随机集形式,并通过递推计算目标状态联合分布的概率假设密度(PHD)来完成.然而,对于被动测角的非线性跟踪问题,PHD无法获得闭合解,为此提出一种新的高斯混合粒子PHD算法.该算法利用高斯混合近似PHD,以避免用聚类确定目标状态,并采用拟蒙特卡罗(QMC)积分方法计算目标状态的预测和更新分布.仿真结果验证了所提出算法的有效性. When the number of targets is unknown or varies with time, multi-target state and measurements are represented as random sets and the multi-target tracking problem is addressed by calculating the probability hypothesis density（PHD） of the joint distribution, recursively. However, PHD can not provide a closed-form solution to the nonlinear problem occurred in the passive bearings-only multi-target tracking system. A new Gaussian mixture particle PHD（GMPPHD） filter is presented in the paper. The PHD is approximated by a mixture of Gaussians, which avoids clustering to determine target states. And Quasi-Monte Carlo integration method is used for approximating the prediction and update distributions of target states. Simulation results show the effectiveness of the proposed algorithm.
作者 张俊根 姬红兵
机构地区 西安电子科技大学电子工程学院
出处 《控制与决策》 EI CSCD 北大核心 2011年第3期413-417,共5页 Control and Decision
基金 国家自然科学基金项目(60871074).
关键词 多目标跟踪 随机集 概率假设密度 被动测角 拟蒙特卡罗积分 multi-target tracking random sets： probability hypothesis density： passive bearings-only Quasi-Monte Carlo integration
分类号 TN953 [电子电信—信号与信息处理]
  • 相关文献

参考文献15

  • 1Bar-Shalom Y, Li X R. Multitarget-multisensor tracking: Principles and techniques[M]. Storrs: YBS Publishing, 1995: 1-277.
  • 2Musicki D, Evans R. Joint integrated probabilistic data association: JIPDA[J]. IEEE Trans on AES, 2004, 40(3): 1093-1099.
  • 3Blackman S S. Multiple hypothesis tracking for multiple target tracking[J]. IEEE Aerospace and Electronic Systems Magazine, 2004, 19(1): 5-18.
  • 4Ruan Y, Willett E Multiple model PMHT and its application to the second benchmark radar tracking problem[J].IEEE Trans on AES, 2004, 40(4): 1337-1350.
  • 5Hue C, le Cadre J P, Perez P. Sequential Monte Carlo methods for multiple target tracking and data fusion[J]. IEEE Trans on Signal Processing, 2002, 50(2): 309-325.
  • 6庄泽森,张建秋,尹建君.Rao-Blackwellized粒子概率假设密度滤波算法[J].航空学报,2009,30(4):698-705. 被引量：17
  • 7Yin J J, Zhang J Q, Zhuang Z S. Gaussian-sum PHD filtering algorithms for nonlinear non-Gaussian models[J]. Chinese J of Aeronautics, 2008, 21(4): 341-351.
  • 8Mahler R P S. Multitarget Bayes filtering via first-order multitarget moments[J]. IEEE Trans on AES, 2003, 39(4): 1152-1178.
  • 9Clark D, Vo B T, Vo B N. Gaussian particle implementations of probability hypothesis density filters[C]. IEEE Aerospace Conf. Big Sky, MT, 2007: 1- 11.
  • 10Vihola M. Rao-blackwellised particle filtering in random set multitarget tracking[J]. IEEE Trans on AES, 2007, 43(2): 689-705.

二级参考文献30

  • 1Schon T, Gustafsson F, Nordlund P J. Marginalized particle filters for mixed linear/nonlinear state-space models[J].IEEE Transaction on Signal Processing, 2005, 53(7): 2279 -2289.
  • 2Schon T, Karlsson R, Gustafsson F. The marginalized particle filter in practice[R]. Linkoping: Linkoping University, LiTH-ISY-R 2715, 2005.
  • 3Anderson B D O, Moore J B. Optimal filtering[M]. Engle wood Ciffs, NJ: Prentice-Hall, 1979.
  • 4Gordon N J, Salmond D J, Smith A F M. Novel approach to nonlinear/non Gaussian Bayesian state estimation [J]. IEE Proceedings F, 1993, 140(2): 107-113.
  • 5Arulampalam M S, Maskelli S, Gordon N, et al. A tutorial on particle filters for online nonlinear non-Gaussian Bayesian tracking[J]. IEEE Transactions on Signal Processing, 2002, 50(2): 174-188.
  • 6Karlsson R, SchOn T. Complexity analysis of the marginalized particle filter[R]. Linkoping: Linkoping University, LiTHISY-R-2611 , 2004.
  • 7Doucet A, Gordon N J, Krishnamurthy V. Particle filters for state estimation of jump Markov linear systems[J]. IEEE Transactions on Signal Processing, 2001, 49(3), 613-624.
  • 8Doucet A. On sequential simulation-based methods for Bayes ian filtering[R]. CUED/F INFENG/TR. 310 (1998), 1998.
  • 9Panta K, Vo B, Singh S. Improved probability hypothesis density (PHD) filter for multitarget traeking[C] // Proceedings of the 2005 Third International Conference on Intelligent Sensing and Information Processing. 2005:213- 218.
  • 10Hoffman J R, Mahler R P S. Multitarget miss distance via optimal assignment[J]. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 2004, 34(3): 327- 336.

共引文献16

同被引文献115

  • 1陈俊超,张俊豪,刘诗佳,陆小锋.基于背景建模与帧间差分的目标检测改进算法[J].计算机工程,2011,37(S1):171-173. 被引量：23
  • 2Chang KC, Barshalom Y.Joint probabilistic data association for multitarget tracking with possibly unresolved measurement and maneuvers[J].IEEE Transactions on Automatic Control,1984,.
  • 3Mahler R.Multitarget Bayes filtering via first-order multitarget moment[J].IEEE Transactions on Aerospace and Electronic Systems,2003,39(4):1152-1178.
  • 4Vo BN, Ma WK.The Gaussian mixture probability hypothesis density filter[J].IEEE Transactions on Signal Processing,2006,:- Vo BN, Singh S, 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.
  • 5Johansen AM, Singh SS, Doucet A, et al..Convergence of the SMC implementation of the PHD filter[J].Methodology and computing in applied probability,2006,:-.
  • 6Gordon NJ, Salmond DJ, Smith AFM.Novel approach to nonlinear/non-gaussian Bayesian state estimation[J].IEE Proceedings-F Radar and Signal Processing,1993,140(2):107-113.
  • 7Arulampalam MS, Maskell S, Gordon N, et al..A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking[J].IEEE Transactions on Signal Processing,2002,:-.
  • 8NIEDERREITER H.Quasi-Monte Carlo methods and pseudo-random numbers[J].Bulletin of The American Mathematical Society,1978,84(6):957-1041.
  • 9James F.A review of pseudorandom number generators[J].Computer Physics Communication,1990,60(3):329-344.
  • 10Wang X, Hickmell F.Randomized Halton sequences[J].Mathematical and Computer Modelling,2000,32(7-8):887-899.

引证文献12

二级引证文献90

控制与决策
2011年 第3期

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部