基于S-D分配的集中式多传感器不敏滤波算法

研究了非线性环境中的集中式多传感器多目标跟踪问题，提出了一种基于S-D分配的集中式多传感器不敏滤波算法。算法通过广义S-D分配技术实现每个传感器中的量测与目标的数据关联，求得所有可能互联中的最佳划分，然后按照顺序多传感器联合概率数据互联算法，依次处理最佳划分中各传感器源于同一目标的量测，在此基础上通过不敏卡尔曼滤波(UKF)解决非线性系统中的目标跟踪问题。最后给出了该算法与MSJPDA/EKF算法的仿真比较，结果表明该算法具有更高的稳定性和跟踪精度。

Centralized Multisensor Unscented Filter Algorithm Based on S-D Assignment

For the problem of multisensor-multitarget tracking in nonlinear system, a novel centralized multisensor unscented filter algorithm based on S-D assignment, SD-CMSUKF, is proposed. In the new algorithm, the association of measurements from each sensor to targets is first implemented according to the generalized S-D assignment technique and the optimal partition can be achieved. Then in the optimal partition, the measurements from the same target are dealt with sequentially in terms of the principle of sequential multisensor joint probabilistic data association algorithm (MSJPDA). Based on these, UKF is used for the propagation of state distribution in nonlinear system and the SD-CMSUKF algorithm is derived. Compared with the MSJPDA/EKF, the accuracy and robustness of the proposed algorithm are improved. Simulation results show the superiority of the new algorithm.