Maximum correntropy Kalman filter

被引:664
作者
Chen, Badong [1 ]
Liu, Xi [1 ]
Zhao, Haiquan [2 ]
Principe, Jose C. [1 ,3 ]
机构
[1] Xi An Jiao Tong Univ, Sch Elect & Informat Engn, Xian, Peoples R China
[2] Southwest Jiaotong Univ, Sch Elect Engn, Chengdu, Peoples R China
[3] Univ Florida, Dept Elect & Comp Engn, Gainesville, FL 32611 USA
基金
中国国家自然科学基金;
关键词
Kalman filter; Maximum correntropy criterion (MCC); Fixed-point algorithm; H-INFINITY; SYSTEMS;
D O I
10.1016/j.automatica.2016.10.004
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Traditional Kalman filter (KF) is derived under the well-known minimum mean square error (MMSE) criterion, which is optimal under Gaussian assumption. However, when the signals are non-Gaussian, especially when the system is disturbed by some heavy-tailed impulsive noises, the performance of KF will deteriorate seriously. To improve the robustness of KF against impulsive noises, we propose in this work a new Kalman filter, called the maximum correntropy Kalman filter (MCKF), which adopts the robust maximum correntropy criterion (MCC) as the optimality criterion, instead of using the MMSE. Similar to the traditional KF, the state mean vector and covariance matrix propagation equations are used to give prior estimations of the state and covariance matrix in MCKF. A novel fixed-point algorithm is then used to update the posterior estimations. A sufficient condition that guarantees the convergence of the fixed-point algorithm is also given. Illustration examples are presented to demonstrate the effectiveness and robustness of the new algorithm. (C) 2016 Elsevier Ltd. All rights reserved.
引用
收藏
页码:70 / 77
页数:8
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