关键词: 联合概率数据互联, 多传感器数据融合, 概率统计加权

Abstract: Aiming at the problem that it is difficult to track multiple targets in complex environment of single sensor joint probabilistic data association(JPDA), this paper proposes a method of measurement-target association probabilistic statistical weighting parallel and sequential multi-sensor data fusion based on JPDA. Firstly, JPDA algorithm of single sensor is given. Then, the mathematical model of multi-sensor JPDA is given. Based on this model and using association probabilistic weighting, the parallel and sequential data fusion formulas are deduced, which have certain guiding significance for multi-sensor data fusion. Finally, the distance RMSE of target tracking is simulated for single sensor JPDA method under different clutter density, process and observation noise. The results show that the distance RMSE of target raise with the increasing of these three indicators. Simultaneously, the pedestrian tracking performance on data set PETS2009 is simulated for two kinds of multi-sensor JPDA methods of this paper and several other tracking methods. The results show that the parallel and the sequential multi-sensor JPDA methods of this paper are superior to other methods in tracking accuracy, tracking position accuracy, track maintenance and track loss. Furthermore, sequential fusion method is slightly better than parallel multi-sensor JPDA method in tracking performance.

Key words: joint probabilistic data association, multi-sensor data fusion, probabilistic statistical weighting