集中式多传感器无极联合概率数据互联算法

针对杂波环境下非线性系统中的多传感器多目标跟踪问题，提出了一种集中式多传感器无极联合概率数据互联算法。该算法中，首先采用无极卡尔曼滤波器实现非线性系统中状态分布的传递，在此基础上应用联合概率数据的思想将单个传感器的量测点迹与航迹互联，最后推广至顺序结构。由于无极卡尔曼滤波器可以获得比扩展卡尔曼滤波算法更高精度的近似，因此能减少非线性模型线性化引起的近似误差对联合概率数据互联概率及状态估计的影响，与基于扩展卡尔曼滤波器思想的顺序多传感器联合概率数据互联算法相比，该算法具有更高的跟踪精度和稳定性，最后通过仿真结果验证了该算法的优越性。

Centralized multisensor unscented joint probabilistic data association algorithm

A novel centralized multisensor unscented joint probabilistic data association algorithm, CMSU-JPDA, is proposed for the muhisensor-multitarget tracking problem of nonlinear systems in a clutter environ-ment. In the new algorithm, UKF is used for the propagation of state distribution in the nonlinear system first, then the association of measurement to track is implemented on the principle of JPDA. Based on this, the CM-SUJPDA algorithm is derived by use of the sequential MSJPDA technique. Because the approximate accuracy of UKF is higher than EKF, the association probability and state estimation in the proposed algorithm are not af-fected by the linearization error. Hence the accuracy and robustness of CMSUJPDA are improved compared with the MSJPDA/EKF. Finally the simulation shows the superiority of the new algorithm.