A Novel Robust Variational Bayesian Filter for Unknown Time-Varying Input and Inaccurate Noise Statistics

被引：5
|
作者：
Huang, Wei [1 ]
Fu, Hongpo [1 ]
Zhang, Weiguo [1 ]
机构：
[1] Northwestern Polytech Univ, Sch Automat, Xian, Peoples R China
来源：
IEEE SENSORS LETTERS | 2023年 / 7卷 / 03期
基金：
中国国家自然科学基金;
关键词：
Probability density function; Heavily-tailed distribution; Gaussian distribution; Sensors; Noise measurement; Covariance matrices; State estimation; Sensor signal processing; state estimation; heavy-tailed noise; robust filter; unknown input; variational Bayesian; KALMAN FILTER; STATE ESTIMATION;
D O I：
10.1109/LSENS.2023.3248172
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Considering that, in many practical applications, the unknown time-varying input and heavy-tailed process and measurement noises induced by some unpredictable anomalous behaviors may degrade the performance of conventional filters seriously, this letter proposes a new robust variational Bayesian (VB) filter. First, the modified one-step prediction and measurement likelihood probability density function are constructed. Then, the VB method is utilized to jointly infer the system state, unknown time-varying input, and inaccurate noise covariance matrices. Finally, a new robust filter is derived, and its effectiveness is verified by the numerical simulations.
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收藏
页数：4
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