A Novel Adaptive Kalman Filter With Unknown Probability of Measurement Loss

被引：44
|
作者：
Jia, Guangle [1 ]
Huang, Yulong [1 ]
Zhang, Yonggang [1 ]
Chambers, Jonathon [1 ,2 ]
机构：
[1] Harbin Engn Univ, Coll Automat, Harbin 150001, Peoples R China
[2] Univ Leicester, Sch Engn, Leicester, Leics, England
来源：
IEEE SIGNAL PROCESSING LETTERS | 2019年 / 26卷 / 12期
关键词：
Adaptive Kalman filter; variational Bayesian; measurement loss; Bernoulli random variable;
D O I：
10.1109/LSP.2019.2951464
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：
A novel variational Bayesian (VB)-based adaptive Kalman filter (AKF) is proposed to solve the filtering problem of a linear system with unknown probability of measurement loss. The sum of two likelihood functions is transformed into an exponential multiplication form, and the state vector, the Bernoulli random variable and the probability of measurement loss are jointly inferred based on the VB approach. Simulation results demonstrate the superiority of the proposed AKF as compared with the existing filtering algorithms with unknown probability of measurement loss.
引用
收藏
页码：1862 / 1866
页数：5
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