Linear and Nonlinear Regression-Based Maximum Correntropy Extended Kalman Filtering

The extended Kalman filter (EKF) is a method extensively applied in many areas, particularly, in nonlinear target tracking. The optimization criterion commonly used in EKF is the celebrated minimum mean square error (MMSE) criterion, which exhibits excellent performance under Gaussian noise assumpti...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on systems, man, and cybernetics. Systems Vol. 51; no. 5; pp. 3093 - 3102
Main Authors: Liu, Xi, Ren, Zhigang, Lyu, Hongqiang, Jiang, Zhihong, Ren, Pengju, Chen, Badong
Format: Journal Article
Language:English
Published: New York IEEE 01.05.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Covariance matrices
Criteria
Extended Kalman filter
Extended Kalman filter (EKF)
fixed-point algorithm
Iterative algorithms
Iterative methods
Kalman filters
Kernel
Linear regression
Mathematical model
maximum correntropy criterion (MCC)
Noise measurement
Nonlinear filters
Optimization
Performance degradation
Random noise
Regression models
Tracking
ISSN:2168-2216, 2168-2232
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The extended Kalman filter (EKF) is a method extensively applied in many areas, particularly, in nonlinear target tracking. The optimization criterion commonly used in EKF is the celebrated minimum mean square error (MMSE) criterion, which exhibits excellent performance under Gaussian noise assumption. However, its performance may degrade dramatically when the noises are heavy tailed. To cope with this problem, this paper proposes two new nonlinear filters, namely the linear regression maximum correntropy EKF (LRMCEKF) and nonlinear regression maximum correntropy EKF (NRMCEKF), by applying the maximum correntropy criterion (MCC) rather than the MMSE criterion to EKF. In both filters, a regression model is formulated, and a fixed-point iterative algorithm is utilized to obtain the posterior estimates. The effectiveness and robustness of the proposed algorithms in target tracking are confirmed by an illustrative example.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2168-2216
2168-2232
DOI:10.1109/TSMC.2019.2917712