An Adaptive Variational Bayesian Algorithm for Measurement Loss for Underwater Navigation

被引：0
|
作者：
Huang, Haoqian [1 ]
Tang, Jiacheng [1 ]
Jin, Yuanfeng [1 ]
机构：
[1] Hohai Univ, Coll Energy & Elect Engn, Nanjing, Peoples R China
来源：
2020 IEEE INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING, COMMUNICATIONS AND COMPUTING (IEEE ICSPCC 2020) | 2020年
关键词：
Measurement loss; state estimation; underwater environment; variational Bayesian;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The marine environment is changeable and complex, and it is difficult but indispensable to study the complex and time-varying environment. The measurement loss has an effect on obtaining the high accuracy navigation information. This paper proposes an adaptive variational Bayesian filter (AVBF) algorithm which takes advantages of the variational Bayesian approach and Kalman filter to deal with the problems of the measurement loss. The proposed AVBF is proved in theory and verified by simulation experiments. Owing to the characteristics of the variational Bayesian approach, the higher precise state information can be acquired by the AVBF compared with the traditional Kalman filter.
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页数：4
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