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多目标滤波中的多传感器概率假设密度算法 被引量：7

Multi-sensor Probability Hypothesis Density Algorithm in Multi-target Filtering
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摘要 多传感器情况下的多目标概率假设密度(PHD)滤波是建立在假设模型上实现的。该文用随机有限集(RFS)方法描述多目标状态空间和传感器量测空间,分析了多传感器通用假设模型下的探测概率、似然函数和杂波分布,在此基础上利用概率产生泛函(PGFL)推导出了多传感器PHD滤波递归式,进而提出粒子标记法多传感器贯序蒙特卡洛PHD(SMC-PHD)滤波等价实现算法,降低了多传感器PHD滤波的计算复杂度。最后给出了算法的具体实现,得到了良好的多目标数目和可跟踪多目标状态的估计。 Multi-target filtering using Probability Hypothesis Density（PHD） in multi-sensor case is based on assumption model to avoid being computationally intractable.Based on describing target state space and sensor observation space by Random Finite Set（RFS） method,and on the analysis of detection probability,likelihood function and clutter distribution under the multi-sensor universal assumption model,the multi-sensor version of multi-target PHD filter is constructed by Probability Generating Functional（PGFL）,the multi-sensor labeling particle Sequential Monte Carlo PHD（SMC-PHD） filtering algorithm is presented to implement this fiter with lower computational complexity.Finally,the better estimation of target number and track-valued state are obtained by simulation.
作者 杨可 傅忠谦 王剑亭 林日钊
机构地区 中国科学技术大学电子科学与技术系 中国科学院上海技术物理研究所
出处 《电子与信息学报》 EI CSCD 北大核心 2012年第6期1368-1373,共6页 Journal of Electronics & Information Technology
基金 中国科学院知识创新工程三期项目(KGCX3-SYW-407-03)资助课题
关键词 多传感器滤波 概率假设密度 概率产生泛函 假设模型 粒子标记法 Multi-sensor filtering Probability Hypothesis Density（PHD） Probability Generating Functional（PGFL） Universal assumption model Labeling particle
分类号 TP391 [自动化与计算机技术—计算机应用技术]
  • 相关文献

参考文献11

  • 1Mahler R. Multitarget Bayes filtering via first-order multitarget moments[J]. IEEE Transactions on Aerospace and Electronic Systems, 2003, 39(4): 1152-1178.
  • 2Mahler R. PHD filter of higher order in target number[J]. IEEE Transactions on Aerospace and Electronic Systems, 2007, 43(4): 1523-1543.
  • 3欧阳成,姬红兵,张俊根.一种改进的CPHD多目标跟踪算法[J].电子与信息学报,2010,32(9):2112-2118. 被引量：12
  • 4Mahler R. A survey of PHD filter implementations[C]. Procof SPIE, 2007, Vol.65 67 656700-1:12.
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  • 10Vo B N, Singh S, and Doucet A. Sequential Monte Carlo methods for multi-target filtering with random finite sets[J]. IEEE Transcations oh Aerospace and Electronic Systems, 2005, 41(4): 1224-1245.

二级参考文献10

  • 1Goodman I,Mahler R,and Nguyen H.Mathematics of Data Fusion[M].Norwell,MA,Kluwer,1997:131-175.
  • 2Mahler R.Multitarget Bayes filtering via first-order multitarget moments[J].IEEE Transactions on Aerospace and Electronic Systems,2003,39(4):1152-1178.
  • 3Vo B N,Singh S,and Doucet A.Sequential Monte Carlo methods for Bayesian multi-target filtering with random finite sets[J].IEEE Transactions on Aerospace and Electronic Systems,2005,41(4):1224-1245.
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  • 8Erdinc O,Willett P,and Coraluppi S.The Gaussian mixture cardinalized PHD tracker on MSTWG and SEABAR'07 datasets[C].11th International Conference on Information Fusion,Cologne,Germany,June 30-July 3,2008:1-8.
  • 9Vo B T,Vo B N,and Cantoni A.Analytic implementations of the cardinalized probability hypothesis density filter[J].IEEE Transactions on Signal Processing,2007,55(7):3553-3567.
  • 10Ulmke M,Erdinc O,and Willett P.Gaussian mixture cardinalized PHD filter for ground moving target tracking[C].10th International Conference on Information Fusion,Quebec,Que,July 9-12,2007:1-8.

共引文献11

同被引文献65

  • 1Mahler R P S. Multi-target Bayes filtering via first- order multi-target moments[J]. IEEE Transactions on Aerospace and Electronic Systems, 2003, 39 (4): 1 152-1 178.
  • 2Mahler R P S. Statistical Multisource Multitarget Informat Ionfusion [M]. Boston: Artech House Publishers, 2007.
  • 3Vo B N, Singh S, Doucet A. Sequential Monte Carlo methods for Bayesian muhi-target fihering with random finite sets [J]. IEEE Transactions on Aerospace and Electronic Systems, 2005, 41(4): 1 224-1 245.
  • 4Sidenbladh H. Multi-target particle filtering for the probability hypothesis density[C]// Processing of 6th International Conference on Information Fusion, Cairns, Australia: IEEE Press, 2003: 800-806.
  • 5Punithakumar K, Kirubarajan T, Sinha A. Multiple- model probability hypothesis density filter for tracking maneuvering targets [ J ]. IEEE Transactions on Aerospace and Electronic Systems, 2008, 44 (1) :87-98.
  • 6Tobias M, 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.
  • 7PashaSA, Vo B N, Tuan H D, et al. A Gaussian mixture PHD filter for jump Markov system models [J]. IEEE Transactions on Aerospace and Electronic Systems, 2009, 45(3): 919-936.
  • 8Panta K, Vo B N, Singh S. Improved probability hypothesis density (PHD) filter for multi-target tracking [ C ]// Proceedings of the IEEE 3rd International Conference on Intelligent Sensing and Information Processing. Bangalore, India: IEEE Press, 2005: 213-218.
  • 9Arulampalam M S, Maskelll 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.
  • 10赵琳,等.非线性系统滤波理论[M].北京:国防工业出版社,2012.

引证文献7

二级引证文献30

电子与信息学报
2012年 第6期

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