Robust Gaussian Filtering based on M-estimate with Adaptive Measurement Noise Covariance

被引：0

作者：

Hu, Baiqing ^{[1
]}

Chang, Lubin ^{[1
]}

Qin, Fangjun ^{[1
]}

机构：

[1] Naval Univ Engn, Dept Nav Engn, Wuhan 430033, Hubei, Peoples R China

来源：

2017 20TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION) | 2017年

基金：

中国国家自然科学基金;

关键词：

Gaussian filtering; M-estimate; Myers -Tapley method; robust; KALMAN FILTER; NAVIGATION; TRACKING;

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：

In this paper, a robust Gaussian filtering is proposed based on M-estimate with adaptive measurement noise covariance. In the proposed method, the M-estimate is incorporated into the Gaussian filtering framework through modifying the measurements residues to introduce robustness. The modified measurements residues are also stored to identify the measurement noise covariance based on Myers-Tapley method. The proposed method can handle both the non-Gaussian measurement noise and inaccurate prior knowledge of the measurement noise covariance.

引用

页码：851 / 856

页数：6

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