偏差未补偿自适应边缘化容积卡尔曼滤波跟踪方法  

An Adaptive Target Tracking Method Utilizing Marginalized Cubature Kalman Filter with Uncompensated Biases

作　　者：邓洪高[1,4] 余润华 纪元法 吴孙勇[2,3] 孙少帅 DENG Honggao;YU Runhua;JI Yuanfa;WU Sunyong;SUN Shaoshuai(School of Information and Communication,Guilin University of Electronic Technology,Guilin 541004,China;Guangxi Key Laboratory of Precision Navigation Technology and Application,Guilin University of Electronic Technology,Guilin 541004,China;School of Mathematics and Computing Science,Guilin University of Electronic Science and Technology,Guilin,541004,China;National&Local Joint Engineering Research Center of Satellite Navigation Positioning and Location Service,Guilin 541004,China)

机构地区：[1]桂林电子科技大学信息与通信学院,桂林541004 [2]桂林电子科技大学广西精密导航技术与应用重点实验室,桂林541004 [3]桂林电子科技大学数学与计算科学学院,桂林541004 [4]卫星导航定位与位置服务国家地方联合工程研究中心,桂林541004

出　　处：《电子与信息学报》2025年第1期156-166,共11页Journal of Electronics & Information Technology

基　　金：广西重点研发项目(AB23026150,AB23026147);国家自然科学基金(U23A20280)。

摘　　要：针对存在突变测量偏差和未知时变量测噪声场景下的目标跟踪问题,该文提出一种偏差未补偿自适应边缘化容积卡尔曼滤波跟踪方法。首先通过建立差分量测方程来消除恒定的测量偏差,同时构建满足beta-Bernoulli分布的指示变量识别突变测量偏差,将相邻时刻目标状态扩维以满足实时滤波需求,利用逆Wishart分布建模未知量测噪声协方差矩阵,从而建立目标状态、指示变量、噪声协方差矩阵的联合分布,并通过变分贝叶斯推断来求解各个参数的近似后验。为减小滤波负担,对扩维后的状态向量进行边缘化处理,结合容积卡尔曼滤波方法实现边缘化容积卡尔曼滤波跟踪。仿真实验结果表明,所提方法能够同时处理突变测量偏差和未知时变量测噪声,从而对目标进行有效跟踪。Objective In radar target tracking,tracking accuracy is often influenced by sensor measurement biases and measurement noise.This is particularly true when measurement biases change abruptly and measurement noise is unknown and time-varying.Ensuring effective target tracking under these conditions poses a significant challenge.An adaptive target tracking method is proposed,utilizing a marginalized cubature Kalman filter to address this issue.Methods(1)Initially,measurements taken at adjacent time points are differentiated to formulate the differential measurement equation,thereby effectively mitigating the influence of measurement biases that are either constant or change gradually between adjacent observations.Concurrently,the target states at these moments are expanded to create an extended state vector facilitating real-time filtering.(2)Following the differentiation of measurements,sudden changes in measurement biases may cause the differential measurement at the current moment to be classified as outliers.To identify the occurrence of these abrupt bias changes,a Beta-Bernoulli indicator variable is established.If such a change is detected,the differential measurement for that moment is disregarded,and the predicted state is adopted as the updated state.In the absence of any abrupt changes,standard filtering procedures are conducted.The Gaussian measurement noise,despite having unknown covariance,continues to follow a Gaussian distribution after differentiation,allowing its covariance matrix to be modeled using the inverse Wishart distribution.(3)A joint distribution is formulated for the target state,indicator variables,and the covariance matrix of the measurement noise.The approximate posteriors of each parameter are derived using variational Bayesian inference.(4)To mitigate the increased filtering burden arising from the high-dimensional extended state vector,the extended target state is marginalized,and a marginalized cubature Kalman filter for target tracking is implemented in conjunction with the cubatu

关 键 词：突变测量偏差 Beta-Bernoulli分布 逆Wishart分布 变分贝叶斯推断 边缘化容积卡尔曼滤波 

分 类 号：V243.2[航空宇航科学与技术—飞行器设计] TN953[电子电信—信号与信息处理]