协方差交集
协方差矩阵
卡尔曼滤波器
衰退
协方差矩阵的估计
扩展卡尔曼滤波器
算法
Wishart分布
快速卡尔曼滤波
集合卡尔曼滤波器
人工智能
计算机科学
统计
数学
协方差
控制理论(社会学)
多元统计
解码方法
控制(管理)
作者
Cheng Pan,Jingxiang Gao,Zengke Li,Nijia Qian,Fangchao Li
出处
期刊:Measurement
[Elsevier]
日期:2021-02-10
卷期号:176: 109139-109139
被引量:45
标识
DOI:10.1016/j.measurement.2021.109139
摘要
Abstract If the system model or the statistical characteristics of noise are inaccurate, the past measurements will directly affect the accuracy of current state estimation or even lead to filtering divergence. To overcome above difficulties, a multiple fading factors-based strong tracking variational Bayesian adaptive Kalman filter is proposed. Firstly, the inverse Wishart distribution is adopted to model the measurement noise covariance matrix. Secondly, the remodified measurement noise covariance matrix and the innovation covariance matrix estimated by exponential weighting method are employed to construct the scalar fading factor. Next, the multiple fading factors are calculated to correct the predicted error covariance matrix. Finally, the local optimal estimations of measurement noise covariance matrix and state are obtained by variational Bayesian approach. The target tracking simulations verify that the proposed algorithm has better tracking ability for the predicted error covariance matrix and the measurement noise covariance matrix compared with the existing filters.
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