期刊文献+

基于STC-IMM结构的自适应多模型跟踪算法 被引量:2

Adaptive multiple-model tracking algorithm based on STC-IMM structure
原文传递
导出
摘要 针对机动目标跟踪问题,基于转换时间条件交互多模型(STC-IMM)结构,提出一种转换概率自适应的STC-AIMM算法.该算法根据滤波器收敛时间预设了模型转换时间条件,保证了滤波器对目标后验状态的合理逼近,同时通过模型转换概率的自适应算法实现了模型与目标运动模式的实时最优匹配.理论和仿真分析结果表明:相比交互多模型(IMM)算法和STC-IMM算法,该算法能够发挥滤波器最优性能,实现模型概率的优化分配,对目标不同强度的机动具有良好的适应性、跟踪稳定性和更高的跟踪精度. An interacting multiple-model algorithm with switch time conditions based on adaptive transition probabilities is proposed for tracking maneuvering targets, which ensures that the filter approximates target posterior state reasonably by presetting switch time conditions of the model. The model can match the target motion well in real time by using transition probabilities adaption algorithm. The theoretic and simulation analysis show that the proposed algorithm can help the filter achieving optimal performance, make the model probability more reasonable and track the target more accurately than IMM and STC-IMM algorithms. At the same time, it performs well on the stability and adaption.
出处 《控制与决策》 EI CSCD 北大核心 2013年第8期1226-1230,共5页 Control and Decision
基金 国家自然科学青年基金项目(61102109) 陕西省自然科学基金项目(2010JM8013)
关键词 机动目标跟踪 多模型 转换时间条件 转换概率 自适应估计 maneuvering target tracking multiple-model switch time conditions transition probabilities adaptiveestimation
  • 相关文献

参考文献10

  • 1Li X R, Jilkov V E A survey of maneuvering target tracking, Part V: Multiple-model methods[J]. IEEE Trans on Aerospace and Electronic Systems, 2005, 41(4): 1254- 1320.
  • 2鉴福升,徐跃民,阴泽杰.多模型粒子滤波跟踪算法研究[J].电子与信息学报,2010,32(6):1271-1276. 被引量:7
  • 3Blom H A P, Bloem E A. Exact Bayesian and particle filtering of stochastic hybrid systems[J]. IEEE Trans on Aerospace and Electronic Systems, 2007, 43(1): 55-70.
  • 4Blom H, Bar-Shalom Y. The interacting multiple model algorithm for systems with Markovian switching coefficients[J]. IEEE Trans on Automatic Control, 1988, 33(3): 780-783.
  • 5Campo L, Mookerjee P, Bar-Shalom Y. State estimation for systems with sojourn-time-dependent Markov model switching[J]. IEEE Trans on Automatic Control, 1991, 36(2): 238-243.
  • 6Blom H. Hybrid state estimation for systems with semi- Markov switching coefficients[C]. Proc of Eur Control Conf. Grenoble, 1991:1132-1137.
  • 7Petrov A I, Zubov A G. Estimation in nonlinear stochastic systems with sudden changes in structure, parameters and state coordinates[J]. Soviet J of Computer and Systems Sciences, 1991, 29(3): 9-28.
  • 8Svensson D, Svensson L. A new multiple model filter with switch time conditons[J]. IEEE Trans on Signal Processing, 2010, 58(1): 11-25.
  • 9Jilkov V P, Li X R. Online Byesian estimation of transition probabilities for Markovian jump system[J]. IEEE Trans on Signal Processing, 2004, 52(6): 1620-1630.
  • 10Gauvain J L, Lee C H. Maximum a posteriori estimation for multivariate Gaussian mixture observations of Markov chains[J]. IEEE Trans on Speech Audio Processing, 1994, 2(2): 291-298.

二级参考文献10

  • 1Blackman S S and Popoli R.Design and Analysis of Modern Tracking System[M].Norwood MA:Artech House,1999:221-252.
  • 2Blom H A P and Bloem E A.Exact Bayesian and particle filtering of stochastic hybrid systems[J].IEEE Transactions on Aerospace and Electronic Systems,2007,43(1):55-70.
  • 3Liang Yan,Wang Zeng-fu,and Cheng Yong-mei,et al..Estimation of Markov jump systems with mode observation one-step lagged to state measurement[C].The 10th International Conference on Information Fusion,Québec City,Canada,9-12 July 2007:1-6.
  • 4Mcginnity S and Irwin G W.Multiple model bootstrap filter for maneuvering target tracking[J].IEEE Transactions on Aerospace and Electronic Systems,2000,36(3):1006-1012.Driessen H and Boers Y.Efficient particle filter for jump Markov nonlinear systems[J].IEE Proceedings.Radar,Sonar and Navigation,2005,152(5):323-326.
  • 5Yacine M and Mohand S D.Genetic algorithm combined to IMM approach for tracking highly maneuvering targets[J].IAENG International Journal of Computer Science,2008,35(1):41-46.
  • 6Driessen H and Boers Y.Efficient particle filter for jump Markov nonlinear systems[J].IEE Proceedings.Radar,Sonar and Navigation,2005,152(5):323-326.
  • 7Doucet A,Gordon N,and Krishnamurthy V.Particle filters for state estimation of jump Markov linear systems[J].IEEE Transactions on Signal Processing,2001,49(3):613-624.
  • 8Caron F,Davy M,and Duflos E,et al..Particle filtering for multisemor data fusion with switching observation models:Application to land vehicle pesitioning[J].IEEE Transactions on Signal Processing,2007,55(6):2703-2719.
  • 9Fredrik G,Niclas B,and Urban F,et al..Particle filters for positioning,navigation and tracking[J].IEEE Transactions on Signal Processing,2002,50(2):425-437.
  • 10刘贵喜,高恩克,范春宇.改进的交互式多模型粒子滤波跟踪算法[J].电子与信息学报,2007,29(12):2810-2813. 被引量:21

共引文献6

同被引文献23

  • 1罗笑冰,王宏强,黎湘.模型转移概率自适应的交互式多模型跟踪算法[J].电子与信息学报,2005,27(10):1539-1541. 被引量:22
  • 2R Mahler.Multitarget Bayes filtering via first-order multitarget moments[J].IEEE Transactions on Aerospace and Electronic Systems,2003,39(4):1152-1178.
  • 3Ba-Ngu Vo,Sumeetpal Singh,Arnaud Doucet.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.
  • 4Ba-Ngu Vo,Wing-Kin Ma.A closed-form solution for the probability hypothesis density filter[C]∥ 2005 7th International Conference on Information Fusion,Philadelphia,PA,USA,2005:856-863.
  • 5K Punithakumar,T Kirubarajan,A Sinha.Multiple-model probability hypothesis density filter for tracking maneuvering targets[J].IEEE Transactions on Aerospace and Electronic Systems,2008,44(1):87-98.
  • 6Ba-Ngu Vo,Ahmed Pasha,Hoang Duong Tuan.A Gaussian mixture PHD filter for nonlinear jump Markov models[C]∥ Proceedings of the 45th IEEE Conference on Decision and Control,San Diego,CA,USA,December,2006:3162-3167.
  • 7Wood,Trevor M.Interacting methods for manoeuvre handling in the GM-PHD filter[J].IEEE Transactions on Aerospace and Electronic Systems,2011,47(4):3021-3025.
  • 8Tobias M,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.
  • 9Daniel E Clark,Judith Bell.Multi-target state estimation and track continuity for the particle PHD filter[J].IEEE Transactions on Aerospace and Electronic Systems,2007,43(4):1441-1453.
  • 10Cheng Ouyang,Hong-bing Ji,Zhi-qiang Guo.Extensions of the SMC-PHD #lters for jump Markov systems[J].Signal Processing,2012,92(6):1422-1430.

引证文献2

二级引证文献11

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