一种面向互联概率加权的JPDA多传感器数据融合方法

针对单传感器联合概率数据互联(Joint Probabilistic Data Association, JPDA)在复杂环境下难以跟踪多个目标的问题，提出一种基于JPDA量测目标互联概率统计加权并行式和序贯式多传感器数据融合方法。首先，给出单传感器JPDA算法。然后，介绍多传感器JPDA数学模型，基于这一模型，使用互联概率加权，推导并行式和序贯式多传感器数据融合公式，这对多传感器数据融合有一定指导意义。最后，对单传感器JPDA方法在不同杂波密度、不同过程和不同观测噪声下目标跟踪的距离RMSE进行仿真，结果表明，随着这3项指标皆增大，目标距离RMSE增大；同时，对本文的2类多传感器JPDA方法与其他几类跟踪方法在数据集PETS2009下有关行人跟踪性能进行仿真，结果表明，本文并行式和序贯式多传感器JPDA方法相较于其他方法在跟踪准确性、跟踪位置准确性、航迹维持以及航迹遗失上皆为最优，而且序贯式融合略优于并行式多传感器JPDA。

A JPDA Multi-sensor Data Fusion Method for Association Probability Weighting

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.