A New Robust Kalman Filter With Adaptive Estimate of Time-Varying Measurement Bias

被引：31
|
作者：
Huang, Yulong [1 ]
Jia, Guangle [1 ]
Chen, Badong [2 ]
Zhang, Yonggang [1 ]
机构：
[1] Harbin Engn Univ, Coll Automat, Harbin 150001, Peoples R China
[2] Xi An Jiao Tong Univ, Inst Artificial Intelligence & Robot, Xian 710049, Peoples R China
来源：
IEEE SIGNAL PROCESSING LETTERS | 2020年 / 27卷
关键词：
Noise measurement; Covariance matrices; Pollution measurement; Random variables; Probability density function; Kalman filters; Estimation; Kalman filter; variational Bayesian; heavy-tailed measurement noise; unknown and time-varying measurement bias; linear system; FUSION;
D O I：
10.1109/LSP.2020.2983552
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：
To better model the non-Gaussian heavy-tailed measurement noise with unknown and time-varying bias, a new Student & x0027;s t-inverse-Wishart (STIW) distribution is presented. The STIW distribution is firstly written as a Gaussian, inverse-Wishart and normal-Gamma hierarchical form, from which a new robust Kalman filter is then derived based on the variational Bayesian method. Simulation results illustrate the potentials of the new derived robust Kalman filter for addressing the above measurement noise.
引用
收藏
页码：700 / 704
页数：5
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