基于距离向BCRLB的加权融合目标跟踪  

Weighted Fusion Target Tracking Based on Distance Oriented BCRLB

作　　者：孙晓拂 吴俊卿 汪飞[1] 周建江[1] 韩清华 SUN Xiaofu;WU Junqing;WANG Fei;ZHOU Jianjiang;HAN Qinghua(Key Laboratory of Radar Imaging and Microwave Photonics Ministry of Education,Nanjing University of Aeronautics and Astronautics,Nanjing 211000,China;School of Artificial Intelligence,Zaozhuang University,Zaozhuang 277000,China)

机构地区：[1]南京航空航天大学雷达成像与微波光子教育部重点实验室,南京211000 [2]枣庄学院人工智能学院,山东枣庄277000

出　　处：《电光与控制》2024年第6期8-13,61,共7页Electronics Optics & Control

基　　金：国家自然科学基金(62271247);航空科学基金(20200020052005);中国博士后科学基金(2023M731756);枣庄学院“青檀学者”人才项目(1020701)。

摘　　要：推导了目标跟踪距离向贝叶斯克拉美罗下界(BCRLB)的表达式,将距离向BCRLB作为目标跟踪性能的量化指标。针对双机目标跟踪场景,采用交互多模型卡尔曼滤波,提出了基于距离向贝叶斯克拉美罗下界的加权融合算法。仿真实验表明,该算法的目标跟踪航迹与真实航迹贴合较好,跟踪过程中均方根误差能快速收敛到一个稳定的值,相比基于信噪比加权融合的目标跟踪算法,该算法的目标跟踪精度更高。The formula of target tracking Bayesian Cramér Rao Lower Bound(BCRLB)in the range direction is derived,and the BCRLB in the range direction is used as a quantitative indicator of target tracking performance.For dual aircraft target tracking scenarios,BDWF,a BCRLB algorithm based on range direction Weighted Fusion is proposed using interactive multi-model Kalman Filter(KF).The simulation experiment shows that:1)The target tracking trajectory of the proposed algorithm fits well with the real trajectory,and the Root Mean Square Error(RMSE)during the tracking process can quickly converge to a stable value;and 2)Compared with the target tracking algorithm based on signal-to-noise ratio weighted fusion,the target tracking accuracy of the proposed algorithm is higher.

关 键 词：多机协同 雷达探测 目标跟踪 贝叶斯克拉美罗下界 

分 类 号：TN953[电子电信—信号与信息处理]