自适应随机权重容积卡尔曼滤波交互多模型目标跟踪  

Adaptive Random Weighted Cubature Kalman Filter andInteractive Multiple Model Algorithm for Target Tracking

作　　者：宋伟 李春菊[2] SONG Wei;LI Chunju(Ningxia Branch,Civil Aviation Air Traffic Management Bureau,Yinchuan 750000,China;Ningxia Vocational Technical College of Industry and Commerce,Yinchuan 750000,China)

机构地区：[1]民航西北地区空中交通管理局宁夏分局,银川750000 [2]宁夏工商职业技术学院,银川750000

出　　处：《电光与控制》2023年第10期46-50,56,共6页Electronics Optics & Control

摘　　要：针对处理复杂机动目标跟踪问题,提出了一种自适应随机权重容积卡尔曼滤波(ARWCKF)算法。该算法作为交互多模型(IMM)算法的预处理过程,对不同运动模型进行滤波。为了算法的稳定性,引入了随机权重因子。使用时变因子调整马尔可夫概率转移矩阵,提高了IMM算法概率转换精度。将所提IMM-ARWCKF算法与IMM-CKF算法对比发现,在处理复杂机动目标问题时,所提算法具有更高的跟踪精度,算法更加稳定。To tackle the problem of complex maneuvering target tracking,an algorithm of Adaptive Random Weighted Cubature Kalman Filter(ARWCKF)is proposed.As the preprocessing process of Interactive Multiple Model(IMM)algorithm,this algorithm conducts filtering on different motion models.In order to improve the stability of the algorithm,a random weighted factor is introduced.A time-varying factor is used to adjust Markov probability transfer matrix,which improves the probability conversion accuracy of IMM algorithm.Compared with IMM-CKF algorithm,the proposed IMM-ARWCKF algorithm has higher tracking accuracy and better stability in dealing with complex maneuvering targets.

关 键 词：航迹跟踪 机动目标 IMM 容积卡尔曼滤波(CKF) 

分 类 号：TN953[电子电信—信号与信息处理] TN713[电子电信—信息与通信工程]