基于Meanshift聚类Bhattacharya观测似然度修正的联合概率数据关联改进算法

为降低多目标航迹聚集时联合概率数据关联（JPDA）联合关联事件的计算复杂度，提出一种基于Meanshift聚类〖CD*2〗Bhattacharya（Bhy）观测似然度修正的JPDA改进算法。利用Meanshift得到聚类中心，据聚类中心与目标预测量测马氏距离形成跟踪门；提出Bhy似然度矩阵，将Meanshift聚类中心与各量测Bhy距离所表征的观测似然度作为确认矩阵小概率事件划分依据，消除确认矩阵中小概率事件对联合关联事件计算复杂度的影响。实验结果表明：多目标航迹聚集时，该算法在减少计算复杂度同时保持了较高关联精度，跟踪性能明显优于经典JPDA。

Improved joint probabilistic data association algorithm based on Meanshift clustering and Bhattacharya likelihood modification

To reduce the calculation complexity of the Joint Probabilistic Data Association (JPDA) joint-association events, due to multiple targets' tracks aggregation, an improved JPDA algorithm, clustering by Meanshift algorithm and optimizing confirmation matrix by Bhattacharya coefficients,was proposed.The clustering center was created by Meanshift algorithm. Then the tracking gate was obtained by calculating Mahalanobis distance between the clustering center and targets' prediction observation. The Bhattacharya likelihood matrix which was as a basis for low probability events was created, consequently the computing complexity of JPDA joint-association events which was related to low probability events was reduced. The experimental results show that the new method is superior to the conventional JPDA both in computational complexity and precision of estimation for multiple targets' tracks aggregation.