Approximate multi-sensor multi-target joint probabilistic data association algorithm applicable to complex information fusion system
适于复杂信息融合系统的近似联合概率数据关联算法

To reduce the incorrect association rate using NN (Nearest Neighbor) algorithm in complex environment in clutter, a new plot-track association algorithm-Approximate Multi-Sensor multi-target Joint Probabilistic Data Association (AMSJPDA) is presented in the paper. It uses all the measurements in the tracking gate and every measurement has its own power. Added the measurements multiplied by their power the near optimal track estimation is achieved. AMSJPDA, based on the Approximate probabilistic Computing (AC) and Direct probabilistic Computing (DC) brought forward by B. Zhou, is the amelioration of MSJPDA and demands less time than MSJPDA. It meets the need of large scale plates and the real-time performance of data fusion system. At the end of the paper the comparison result of AMSJPDA and the NN is given.