基于SIS框架和蚁群算法的非线性多目标跟踪
doi: 10.3724/SP.J.1146.2007.00688
Non-linear Multi-target Tracking Based on SIS Framework and Ant Colony Optimization
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摘要: 该文提出一种新的非线性多目标跟踪方法用蚁群算法实现数据关联和SIS(Sequential Importance Sampling)实现对单目标的跟踪。首先根据数据关联问题对蚁群算法进行修改，考虑目标运动中的约束条件对关联概率的影响，重新定义蚁群算法中的路径和路径长度，从而利用蚁群算法寻找最短路径的能力实现对数据关联。由于SIS框架是针对非线性系统的一种较好的状态估计方法，该文将其作为对单目标进行跟踪的框架，和蚁群算法共同解决非线性情况下的多目标跟踪问题。实验对一维平面和二维平面中的多个目标进行了仿真，结果表明，将蚁群算法应用于解决数据关联问题是行之有效的。Abstract: A new method based on ACA (Ant Colony Algorithm) is proposed for data association in multi-target tracking. Firstly, the ACA is modified according to specific data association rule, in which the path of ACA and the length of the path are redefined by considering the effect of target moving characteristics on the association possibility. Then the ACA could be applied to find the best tour to the data association problem. Since SIS (Sequential Importance Sampling) performs well in non-linear tracking system, this paper employs it to track targets after achieving the association result with ACA. In computer simulation, examples for multi-target tracking in one-dimension and two-dimension situation are presented. Experimental results show the feasibility and effectiveness of the proposed method.
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[1] Mori S, Chong C, Tse T, and Wisher R. Tracking and classifyingmultiple targets without a priori identification. IEEE Trans. onAutomatic, Control, 1986, AC-31(5): 401-409. [2] Fortmann T, Bar-Shalom Y, and Scheffe M. Sonar tracking ofmultiple targets using joint probabilistic data association[J].IEEE Journal of Oceanic Engineering.1983, 8(3):173-184 [3] Dorigo M, Maniezzo V, and Colorni A. Positive feedback as asearch strategy. Technical Report 91-013, Dipartimento diElettronica, Politecnico Milano, IT, 1991. [4] Dorigo M, Birattati M, and Stiizale T. Ant colonyoptimization. IEEE Computational Intelligence Magazine,2006, 1(11): 28-39. [5] Sanjeev Arulampalam M, Maskell S, and Gordon N, et al.. Atutorial on particle filters for online nonlinear non-gaussianBayesian tracking[J].IEEE Trans. on Signal Processing.2002,50(2):174-188 [6] Chummun M R, Kirubarajan T, and Pattipati K R, et al..Fast data association using multidimensional assignmentwith clustering[J].IEEE Trans. on Aerospace and ElectronicSystems.2001, 37(3):898-913 [7] 段海滨. 蚁群算法原理及其应用. 北京: 科学出版社, 2004:29-36. -
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