A novel nonlinear multisensor multitarget tracking algorithm

A novel multisensor order statistic unscented probabilistic data association algorithm, MSOSUPDA, is proposed for the multsisensor multitarget tracking problem of nonlinear system in clutter. In the new algorithm, the problem of interest is first translated into multiple nonlinear single-sensor multitarget tracking problems, which can be dealt with sequentially. Then UKF is used for the propagation of state distribution in nonlinear system. Based on these ,the association of measurements of single sensor to tracks is implemented according to the principle of order statistics probabilistic data association (OSPDA) and the MSOSUPDA algorithm is derived. Compared with the MSJPDA/EKF, the accuracy and robustness of MSOSUPDA are improved. Furthermore, computational complexity of the proposed algorithm decreases obviously on account of the use of OSPDA. According to the simulation results, the ratio of divergence and the processing time of our proposed algorithm to those of the MSJPDA/EKF algorithm are 19 and 70 percent respectively besides more favorable accuracy.