反向预测加权邻域数据关联算法

标准最近邻域数据关联算法在杂波环境下可能出现误跟踪和丢失目标的现象。综合考虑相关波门内所有候选回 波，提出了反向预测加权邻域数据关联算法。通过计算候选回波反向预测新息范数，归一化后作为各候选回波的加权系数， 然后将候选回波加权和作为等效回波，并对目标的状态进行更新。该算法有效降低了最近邻域算法中误关联对跟踪效果的影 响。仿真结果表明，该算法在保持较少计算量的同时，可有效避免误跟踪和丢失目标。

Reverse Prediction Weighted Neighbor Data Association Algorithm

The standard nearest-neighbor data association algorithm may generate miss-tracking and lose target in a clutter environment. To handle this problem, this paper proposes a reverse prediction weighted neighbor data association algorithm for considering all candidate echoes. After calculating the candidate echoes'reverse prediction residual norm, the normalized weight for each candidate echo is obtained. The equivalent echo that is weighted sum of candidate echoes is used to update the target state. The algorithm effectively reduces the error association by using equivalent echo. The simulation results show that the algorithm can keep less amount of calculation and is effective to avoid miss-tracking and lose target.