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基于随机采样的多量测目标跟踪算法

刘伟峰 柴中 文成林

刘伟峰, 柴中, 文成林. 基于随机采样的多量测目标跟踪算法. 自动化学报, 2013, 39(2): 168-178. doi: 10.3724/SP.J.1004.2013.00168
引用本文: 刘伟峰, 柴中, 文成林. 基于随机采样的多量测目标跟踪算法. 自动化学报, 2013, 39(2): 168-178. doi: 10.3724/SP.J.1004.2013.00168
LIU Wei-Feng, CHAI Zhong, WEN Cheng-Lin. Multi-measurement Target Tracking by Using Random Sampling Approach. ACTA AUTOMATICA SINICA, 2013, 39(2): 168-178. doi: 10.3724/SP.J.1004.2013.00168
Citation: LIU Wei-Feng, CHAI Zhong, WEN Cheng-Lin. Multi-measurement Target Tracking by Using Random Sampling Approach. ACTA AUTOMATICA SINICA, 2013, 39(2): 168-178. doi: 10.3724/SP.J.1004.2013.00168

基于随机采样的多量测目标跟踪算法

doi: 10.3724/SP.J.1004.2013.00168
详细信息
    通讯作者:

    刘伟峰

Multi-measurement Target Tracking by Using Random Sampling Approach

  • 摘要: 在多目标跟踪领域,传统算法假设目标是点源辐射体,至多产生一个量测点,随着现代 传感器技术的发展,可以获得一个目标的多个量测. 本文研究当目标具有一定刚体几何形状并产生 多量测的问题,这类目标称为多量测目标.首先,通过建立目标形状的刚体参数模型,提出采用参数马 尔科夫链采样的方法,估计目标的形状参数.其次,采用等效量测方法,获得目标形心 点的运动状态. 针对目标个数未知情况,在形状目标量测满足泊松分布假设条件下,采用泊松强度比方法获得目标的个数估计. 本文定义了目标类型概率并给出 了目标类型概率的递推算法. 最后,通过三个具有不同形状和分布的多量测目标在二维平面的匀速(Constant velocity, CV)运动进行验证说明,实验表明: 所给方法在目标运动状态估计方面能够获得比较高的估计精度,目标形状估计能够比较稳定精确地估计目标形状的变化. 此外, 500次蒙特卡洛(Monte Carlo, MC)仿真实验表明,多量测目标的跟踪丢失率约为1.4%.
  • [1] Bar-Shalom Y. Tracking methods in a multitarget environment. IEEE Transactions on Automatic Control, 1978, 23(4): 618-626[2] Reid D B. An algorithm for tracking multiple targets. IEEE Transactions on Automatic Control, 1979, 24(6): 843-854[3] Koch W, van Keuk G. Multiple hypothesis track maintenance with possibly unresolved measurements. IEEE Transactions on Aerospace and Electronic Systems, 1997, 33(3): 883-892[4] Koch J W. Bayesian approach to extended object and cluster tracking using random matrices. IEEE Transactions on Aerospace and Electronic Systems, 2008, 44(3): 1042-1059[5] Feldmann M, Frnken D, Koch W. Tracking of extended objects and group targets using random matrices. IEEE Transactions on Signal Processing, 2011, 59(4): 1409-1420[6] Richter E, Obst M, Noll M, Wanielik G. Tracking multiple extended objects — a Markov chain Monte Carlo approach. In: Proceedings of the 14th International Conference on Information Fusion. Chicago. Illinois, USA: IEEE, 2011. 314-321[7] Baum M, Hanebeck U D. Shape tracking of extended objects and group targets with star-convex RHMs. In: Proceedings of the 14th International Conference on Information Fusion. Chicago, Illinois, USA: IEEE, 2011. 338-345[8] Baum M, Noack B, Hanebeck U D. Extended object and group tracking with elliptic random hypersurface models. In: Proceedings of the 13th International Conference on Information Fusion. Edinburg, UK: IEEE, 2010. 1-8[9] Baum M, Hanebeck U D. Random hypersurface models for extended object tracking. In: Proceedings of the 9th IEEE International Symposium on Signal Processing and Information Technology. Ajman, United Arab Emirates: IEEE, 2009. 178-183[10] Mahler R. PHD filters for nonstandard target I: extended targets. In: Proceedings of the 12th International Conference on Information Fusion. Seattle, WA, USA: ISIF, 2009. 915-921[11] Lundquist C, Granstrm K, Orguner U. Estimating the shape of targets with a PHD filter. In: Proceedings of the 14th International Conference on Information Fusion. Chicago, Illinois, USA: IEEE, 2011. 49-56[12] Orguner U. Lundquist C, Granstrm K. Extended target tracking with a cardinalized probability hypothesis density filter. In: Proceedings of the 14th International Conference on Information Fusion. Chicago, Illinois, USA: IEEE, 2011. 65-72[13] 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)[14] Rasmussen C, Hager G D. Probabilistic data association methods for tracking complex visual objects. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23(6): 560-576[15] Joo S W, Chellpa R. A multiple-hypothesis approach for multiobject visual tracking. IEEE Transactions on Image Processing, 2007, 16(11): 2849-2854[16] Fleuret F, Berclaz J, Lengagne R, Fua P. Multicamera people tracking with a probabilistic occupancy map. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008, 30(2): 267-282[17] Gordon N J, Samlond D J, Smith A F M. Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceeding Control Theory and Application, 1993, 140(2): 107-113[18] Oh S, Russell S, Sastry S. Markov chain Monte Carlo data association for multi-target tracking. IEEE Transactions on Automatic Control, 2009, 54(3): 481-497[19] Khan Z, Balch T, Dellaert F. MCMC-based particle filtering for tracking a variable number of interacting targets. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(11): 1805-1819[20] Liu Wei-Feng. Research on Multitarget Tracking Algorithm Based on Random Finite Sets and Finite Mixture Models [Ph.D. dissertation], Xi'an Jiaotong University, China, 2009 (刘伟峰. 基于随机有限集和有限混合模型的多目标跟踪算法研究 [博士学位论文], 西安交通大学, 中国, 2009)[21] Liu W F, Han C Z. Multitarget tracking algorithm based on finite mixture models and equivalent measurement. In: Proceedings of the 11th International Conference on Information Fusion. Cologne, Germany: IEEE, 2008. 1544-1551[22] Hastings W K. Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 1970, 57(1): 97-109[23] Green P J. Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika, 1995, 82(4): 711-732
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出版历程
  • 收稿日期:  2011-10-26
  • 修回日期:  2012-04-28
  • 刊出日期:  2013-02-20

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