一种实现多机动目标跟踪的JPDA-STF算法  

A JPDA-STF Algorithm for Tracking Multiple Maneuvering Targets

作　　者：王宝宝 何晨 张辉[1] 吴盘龙[2] WANG Baobao;HE Chen;ZHANG Hui;WU Panlong(No.716 Research Institute of CSIC,Lianyungang 222006 China;Nanjing University of Science and Technology,Nanjing 210094 China)

机构地区：[1]中国船舶重工集团公司第七一六研究所,江苏连云港222006 [2]南京理工大学,南京210094

出　　处：《电光与控制》2021年第5期11-13,69,共4页Electronics Optics & Control

基　　金：国家自然科学基金(61473153);江苏省自然科学基金(BK 20131352)。

摘　　要：联合概率数据关联(JPDA)算法在解决多目标跟踪时需要目标准确的动力学模型,动力学模型失配在多机动目标跟踪中时常发生,而作为有效的解决方法之一——强跟踪滤波(STF)是针对无杂波环境下的单机动目标设计的。为了提高杂波环境下多机动目标跟踪精度,提出一种联合概率数据关联强跟踪滤波(JPDA-STF)算法。该算法为了能够实现各个目标的渐消因子计算,采用对与目标关联的量测进行加权融合的方式获取目标新息协方差,其中,量测权重的计算则是通过JPDA的方式获取。通过各目标渐消因子获取状态预测协方差,随后在卡尔曼滤波框架下即可实现目标状态的更新。实验结果表明,该算法相比于传统的JPDA算法能够更有效地降低跟踪误差。The Joint Probabilistic Data Association(JPDA)algorithm needs accurate dynamic model of the target when solving the problem of multi-target tracking.However the dynamic model mismatch often occurs in tracking multiple maneuvering targets.As one of the effective solutions the Strong Tracking Filter(STF)is designed for a single maneuvering target in an environment without clutter.In order to improve the tracking accuracy of multiple maneuvering targets in clutter environment a Joint Probabilistic Data Association and Strong Tracking Filter(JPDA-STF)algorithm is proposed.In order to obtain the fading factor of each target the algorithm uses the weighted fusion of the measurement which is associated with the target to obtain the target innovation covariance and the measurement weight is calculated by JPDA.The state prediction covariance can be obtained through the fading factor and then the target state can be updated under the Kalman filter framework.The experimental results show that this algorithm has higher tracking accuracy than the JPDA.

关 键 词：目标跟踪 多机动目标 强跟踪滤波 JPDA JPDA-STF 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]