Fast Algorithm for Data Association Based on Fuzzy Information Fusion
Received date: 2010-05-15
Online published: 2025-05-30
王树亮 , 阮怀林 . 基于模糊信息融合的快速数据关联算法[J]. 弹箭与制导学报, 2011 , 31(1) : 201 -203,210 . DOI: 10.15892/j.cnki.djzdxb.2011.01.020
For overcoming the tracking problem in dense cluster, target's characteristic of distance and bearing were fused by fuzzy technology. With the fusion method, an improved probability data association algorithm was proposed. Compared with the effec tive joint probability data association algorithm in dense cluster, the proposed algorithm is more efficient in engineering application without generating all the association events. Simulation results demonstrate that the tracking performance of this algorithm is well under different condition.
| [1] | Singer RA, Sea R G. A new filter for optimal tracking in dense multi-target environments[C]// Proceedings of the Ninth Allerton Conference Circuit and System Theory, Urbana: |
| [2] | Bar-shalom Y, Jaffer A G. Adaptive nonlinear filtering for tracking with measurements of uncertain origin[C]// Proceedings of the 11th IEEE Conference on Decision and Control, 1972: 243-247. |
| [3] | Bar-shalom Y. Extension of the probabilistic data association filter in multi-target tracking[C]// Proceedings of the 5th Symp. On Nonlinear Estimation, 1974: 16-21. |
| [4] | 袁刚才, 吴永强. 密集杂波环境下的快速数据关联算法[J]. 系统仿真学报, 2006, 18(3): 561-564. |
| [5] | Bar-shalom Y. Multitarget multisensor tracking: Advanced application[M]. Norwood: Artech House, 1990: 1-23. |
| [6] | 杨国胜, 侯朝桢, 窦丽华. 联合概率数据关联中双门限跟踪算法研究[J]. 系统工程与电子技术, 2002, 24(8): 86-89. |
| [7] | 刘双全, 李修和, 贺平. 密集杂波环境下多目标数据关联算法研究[J]. 电子信息对抗技术, 2009, 24(4): 17-19. |
| [8] | 杨万海. 多传感器数据融合及其应用[M]. 西安: 西安电子科技大学出版社, 2006: 88-92. |
| [9] | 杨纶标, 高英仪. 模糊数学原理及应用[M]. 广州: 华南理工大学出版社, 2005: 50-51. |
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