Adaptive Kalman Filtering for Recursive Both Additive Noise and Multiplicative Noise

被引:30
作者
Yu, Xingkai [1 ]
Li, Jianxun [2 ]
机构
[1] Tsinghua Univ, Dept Precis Instrument, Beijing 100084, Peoples R China
[2] Shanghai Jiao Tong Univ, Dept Automat, Shanghai 200240, Peoples R China
基金
中国国家自然科学基金;
关键词
Kalman filters; Noise measurement; Additives; Additive noise; Covariance matrices; Bayes methods; Probability density function; Adaptive Kalman filter; additive process and measurement noises; convergence; multiplicative measurement noise; unknown covariances of multiplicative and additive noises; variational Bayesian (VB) inference; ALGORITHM; STABILITY; SYSTEMS; MODEL; PRODUCTS;
D O I
10.1109/TAES.2021.3117896
中图分类号
V [航空、航天];
学科分类号
08 ; 0825 ;
摘要
This article focuses on the adaptive Kalman filtering problem for linear systems with unknown covariances of both dynamic multiplicative noise (multiplicative measurement noise) and additive noises (additive process and measurement noises). A recursive-noise adaptive Kalman filter is proposed to estimate both states and covariances of noises by using the variational Bayesian (VB) inference and an indirect method. First, we characterize inverse Wishart priors for both measurement noise covariance and process noise covariance and employ the Student's t-distribution to represent the likelihood function, which is non-Gaussian and affected by mixing multiplicative noise and additive measurement noise. Then, an adaptive Kalman filtering for recursive both noise covariance matrices and dynamic state is proposed following VB inference. Performance analysis for VB procedures and the proposed filter is provided to ensure the convergence and stability. A target tracking example is provided to validate the effectiveness of the proposed filtering algorithm.
引用
收藏
页码:1634 / 1649
页数:16
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