修正并行式多传感器不敏多假设跟踪算法

为了有效解决非线性系统中的多传感器多目标跟踪问题，提出了一种修正并行式多传感器不敏多假设滤波算法。算法运用概率数据互联的思想对各传感器的估计量进行概率加权，克服了并行式多传感器算法的误差积累现象，得到了一种修正的多传感器并行式算法。各传感器中量测点迹与航迹的数据互联问题通过多假设方法予以解决，并通过不敏卡尔曼滤波器完成非线性系统中的目标跟踪。仿真结果表明，从跟踪精度及稳定性方面看，所提出的算法性能要优于MSJPDA/EKF算法。

Modified parallel multisensor unscented multiple hypothesis tracking algorithm

For the problem of multisensor multitarget tracking in nonlinear systems, a modified parallel implementation of multisensor unscented multiple hypothesis tracking algorithm (MPUMHT) is proposed. In the new algorithm, in order to solve the problem of error cumulation of the parallel multisensor algorithm, the estimation from each sensor is weighted according to the method of probabilistic data association first and a modified parallel multisensor algorithm is derived. Then a multiple hypothesis tracking method is used for the association of measurements to tracks. Finally, the problem of target tracking in nonlinear systems is implemented according to unscented Kalman filter (UKF), and the MPUMHT algorithm is derived. According to the simulation results, the accuracy and robustness of the proposed algorithm are improved compared with the multisensor joint probabilistic data association/extend Kalman filter (MSJPDA/EKF) algorithm.