雷达科学与技术2024，Vol.22Issue(3)：291-299,9.DOI:10.3969/j.issn.1672-2337.2024.03.007
基于参数解耦的变分贝叶斯自适应卡尔曼滤波
Variational Bayesian Adaptive Kalman Filtering Algorithm Based on Parameter Decoupling Method
摘要
Abstract
In the context of state estimation problems under mismatched noise covariance matrices,a parameter-decoupled variational Bayesian adaptive Kalman filter(PD-VB-AKF)algorithm is proposed in the paper within the framework of variational Bayesian(VB)method.The filter can be applicable when both the process noise covariance matrix(PNCM)and the measurement noise covariance matrix(MNCM)are unknown.The proposed algorithm chooses the predicted error covariance matrix(PECM)as the variable to optimize through variational techniques and introduces a Markov evolution model to construct the parameter-decoupled variational inference model.Furthermore,it utilizes the fixed-point iteration optimization to solve the joint posterior probability distribution of the state,PECM and MNCM,and outlines the convergence criteria of the algorithm.The simulation results validate the effectiveness of proposed algorithm.关键词
自适应状态估计/卡尔曼滤波/变分贝叶斯/噪声协方差矩阵/参数解耦Key words
adaptive state estimation/Kalman filtering/variational Bayesian/noise covariance matrices/parame-ter decoupling分类
信息技术与安全科学引用本文复制引用
许红,刘欣蕊,邢逸舟,全英汇..基于参数解耦的变分贝叶斯自适应卡尔曼滤波[J].雷达科学与技术,2024,22(3):291-299,9.基金项目
国家自然科学基金(No.62301408) （No.62301408）
博士后科学基金(No.2022M722503) （No.2022M722503）
