针对联合概率数据关联算法需要知道目标总数并要对矩阵差分,且计算量呈指数增长的问题,提出了一种模糊多门限概率关联算法,该算法利用测量与目标的关联概率来替代概率数据关联算法中可行联合事件概率的计算,改善性能的同时又减少了计算量。仿真结果表明该算法能有效的解决密集目标跟踪环境下的数据融合精度和收敛性问题,且计算量小。

According to the problems that the joint probability data association arithmetic needs the total number of targets and difference, and the computation capacity takes on exponential increase trend, a fuzzy and multi-gate limit probability data association arithmetic which uses the associated probability of target and measurement for calculation instead of the feasible associated incident probability was proposed. The results of the emulation indicate that the arithmetic is an effective solution with less calculation to multi-sensor data fusion of precision and convergence issues in intensive tracking situation.