多传感器顺序统计量不敏概率数据互联算法  

Multi-sensor Order Statistics Unscented Probabilistic Data Association Algorithm

作　　者：管旭军[1,2] 芮国胜[1] 张玉玲 周旭[1] 

机构地区：[1]海军航空工程学院电子信息工程系,山东烟台264001 [2]海军湛江保障基地通信雷达声纳修理厂,广东湛江524009

出　　处：《光电工程》2009年第8期16-22,共7页Opto-Electronic Engineering

基　　金：国家自然科学基金资助项目(60572161)

摘　　要：针对非线性系统中杂波环境下的多传感器多目标跟踪问题,提出了一种多传感器顺序统计量不敏概率数据互联算法(MSOSUPDA)。算法首先根据顺序结构多传感器系统实现方法将研究问题转化为顺序处理多个非线性单传感器多目标跟踪问题,然后结合顺序统计量概率数据互联(OSPDA)的思想将单个传感器的量测点迹与多个航迹互联,在此基础上采用不敏卡尔曼滤波(UKF)实现非线性条件下目标状态估计与协方差的递推。与MSJPDA/EKF算法相比,算法具有更高的跟踪精度和稳定性,计算量明显减小。仿真结果表明,该算该发散率与耗时分别为MSJPDA/EKF算法的19%与70%,算法综合性能明显好于MSJPDA/EKF算法。A novel Multi-sensor Order Statistic Unscented Probabilistic Data Association Algorithm （MSOSUPDA） is proposed for the multi-sensor multi-target tracking problem of nonlinear system in clutter. In the new algorithm, the problem of interest is first translated into multiple nonlinear single-sensor multi-target tracking problems. Then, the association of measurements of single sensor to multiple tracks is implemented according to the principle of Order Statistics Probabilistic Data Association （OSPDA）. Based on these, Unscented Kalman Filter （UKF） is used for the propagation of state distribution in nonlinear system and the MSOSUPDA is derived. Compared with the MSJPDA/EKF, the accuracy and robustness of MSOSUPDA are improved. Furthermore, computation complexity of the proposed algorithm is decreased obviously on account of the use of OSPDA. According to the simulation results, the divergence ratio and the processing time of our proposed algorithm are equal to 19 and 70 percent of the MSJPDA/EKF algorithm respectively.

关 键 词：不敏卡尔曼滤波器 顺序统计量概率数据互联 多传感器 多目标跟踪 非线性 

分 类 号：V556[航空宇航科学与技术—人机与环境工程]