基于车辆跟踪的数据关联算法研究
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摘要
对于道路车辆目标的跟踪,由于其高机动性、杂波密集等特点,交互式多模型联合概率数据关联无疑是一种好的跟踪算法。但其固有的缺点是计算量会随着目标个数的增多迅速增加,并且其较少的运动模型难以保证跟踪精度,而增加模型个数会引起模型之间的竞争。因此,提出了一种改进的变结构多模型联合数据关联算法(VSMM-JPDA)。理论分析与对比仿真表明:在非机动、机动转弯和直线机动情况下,该算法对目标的跟踪精度都优于固定结构多模型算法,并且运算量大大减少。
For tracking of road vehicle targets, due to its high maneuverability and clutter-intensive characteristics, interactive multi-model joint probability data association is a good tracking algorithm. However, its inherent disadvantage is that as the number of objects increases, its computational complexity will increase rapidly, and its fewer motion models will not be able to guarantee the tracking accuracy, while increasing the number of models will cause competition among the models. Therefore, an improved Variable Structure Multi-Model Joint Data Association Algorithm(VSMM-JPDA) is put forward. The theoretical analysis and comparison simulation show that the proposed algorithm has better tracking accuracy than the fixed structure and multi-model algorithm under non-motoring, maneuvering and linear maneuvering conditions, and the computational cost is greatly reduced.
For tracking of road vehicle targets, due to its high maneuverability and clutter-intensive characteristics, interactive multi-model joint probability data association is a good tracking algorithm. However, its inherent disadvantage is that as the number of objects increases, its computational complexity will increase rapidly, and its fewer motion models will not be able to guarantee the tracking accuracy, while increasing the number of models will cause competition among the models. Therefore, an improved Variable Structure Multi-Model Joint Data Association Algorithm(VSMM-JPDA) is put forward. The theoretical analysis and comparison simulation show that the proposed algorithm has better tracking accuracy than the fixed structure and multi-model algorithm under non-motoring, maneuvering and linear maneuvering conditions, and the computational cost is greatly reduced.
引文
[1]De Feo M, Graziano A, Miglioli R, et al.IMMJPDA versus MHT and Kalman filter with NN correlation:performance comparison[J].IEE Proc-Radar Sonar Navig.,1997,144(2):49-56.
[2]李辉,沈莹,张安,等交互式多模型目标跟踪的研究现状及发展趋势[J].火力与指挥控制,2006,31(11):1-4.
[3]Chan B,Tugnait J K. Tracking of multiple maneuvering targets in clutter using IMM/JPDA filtering and fixed-lag smoothing[J].Automatica,2001,37:239-249.
[4]张晶炜,熊伟,何友.集中交互式多传感器联合概率数据互联算法[J].光电工程,2006,33(11):26-30.
[5]张晶炜,何友,熊伟.集中交互式多传感器模糊联合概率数据互联算法[J].电子学报,2008(8):1655-1659.
[6]周宏仁,敬忠良.机动目标跟踪[M].北京:国防工业出版社,1991.
[7]Li X R. Survey of maneuvering target tracking. Part 1:dynamic models[J]. IEEE Transactions on Aerospace and Electronic Systems,2003, 39(4):1333-1364.
[8]郭睿利,郭云飞,张云龙,等.基于一种改进IMMJPDA算法的地面目标跟踪[J].信息与电子工程, 2012,10(4):406-411.
[2]李辉,沈莹,张安,等交互式多模型目标跟踪的研究现状及发展趋势[J].火力与指挥控制,2006,31(11):1-4.
[3]Chan B,Tugnait J K. Tracking of multiple maneuvering targets in clutter using IMM/JPDA filtering and fixed-lag smoothing[J].Automatica,2001,37:239-249.
[4]张晶炜,熊伟,何友.集中交互式多传感器联合概率数据互联算法[J].光电工程,2006,33(11):26-30.
[5]张晶炜,何友,熊伟.集中交互式多传感器模糊联合概率数据互联算法[J].电子学报,2008(8):1655-1659.
[6]周宏仁,敬忠良.机动目标跟踪[M].北京:国防工业出版社,1991.
[7]Li X R. Survey of maneuvering target tracking. Part 1:dynamic models[J]. IEEE Transactions on Aerospace and Electronic Systems,2003, 39(4):1333-1364.
[8]郭睿利,郭云飞,张云龙,等.基于一种改进IMMJPDA算法的地面目标跟踪[J].信息与电子工程, 2012,10(4):406-411.