基于数据压缩的多传感器不敏滤波算法  

Multisensor Unscented Filter Algorithm Based on Data Compression

作　　者：管旭军[1,2] 芮国胜[1] 周旭[1] 张玉玲[2] 

机构地区：[1]海军航空工程学院电子信息工程系 [2]92854部队

出　　处：《武汉大学学报（信息科学版）》2010年第4期472-476,共5页Geomatics and Information Science of Wuhan University

基　　金：国家自然科学基金资助项目(60572161)

摘　　要：针对非线性系统中杂波环境下的集中式多传感器多目标跟踪问题,提出了一种基于数据压缩技术的多传感器不敏滤波算法。仿真结果表明,与MSJPDA/EKF算法相比,本文提出的算法具有更高的跟踪精度和稳定性,同时所选取的粗关联准则使算法的计算量减少了62%。A novel multisensor multitarget unscented filter algorithm based on data compression, SI）-DCUKF, is proposed for the centralized multsisensor multitarget tracking problem of nonlinear system in clutter. In the new algorithm, the measurements from multiple sensors are first combined according to the rule of generalized S D assignment algorithm and the optimal partition can be achieved. In order to reduce the computation burden, a new coarse association rule is proposed for S-D assignment. Then in the optimal partition, the measurements from the same target are dealt with by use of the method of data compression. Based on these, UKF is used for the propagation of state distribution in nonlinear system and the SD-DCUKF algorithm is derived. According to the simulation results, the accuracy and robustness of proposed algorithm are improved compared with the MSJPDA/EKF algorithm. Furthermore, the method of coarse association proposed makes the computation time decrease by 62 percent.

关 键 词：不敏卡尔曼滤波 数据关联 多传感器 多目标跟踪 非线性 

分 类 号：P228.41[天文地球—大地测量学与测量工程] TN95[天文地球—测绘科学与技术]