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%.Abstract: In multi-target tracking field, conventional algorithms supposed that target is a point source and produces at most one measurement. While with the development of modern sensor technology, a target may give multiple measurements. In this paper, we consider that targets have certain geometrical shapes and give multiple measurements and call these targets multi-measurement targets (MMTS). We first build rigid models for the targets in parameter space and then estimate their parameters using the Markov chain sampling approach. Next, we derive the moving state described by target's centroid with our proposed equivalent measurement. When the number of targets remains unknown, under the Poisson assumption of number of target measurements, we use the ratios of Poisson intensities to estimate the number of targets. We also define the probabilistic vectors of type (PVOT) and propose a recursive process for the PVOT. To verify the proposed algorithm, the final experiment proposes three targets, with different shapes and distributions, moving in a 2-dimension plane with constant velocity (CV). The experimental results show that the estimation of target state has an excellent precision and the shape estimation can better and stably reflect the change of target shape. Besides, the target lost rate is around 1.4% in 500 Monte Carlo (MC) runs.
-
[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
点击查看大图
计量
- 文章访问数: 1393
- HTML全文浏览量: 22
- PDF下载量: 881
- 被引次数: 0