基于最大熵方法的鲁棒自适应滤波及其应用

doi:10.3969/j.issn.1001-506X.2020.03.022

Abstract

Abstract:

Aiming at the problem that the estimation accuracy is degraded as the process noise and measurement noise are affected by impulse noise and becoming non-Gaussian distribution and the noise statistical characteristics is inaccurate, a maximum correntropy variational Bayes adaptive Kalman filter (MCVBAKF) is proposed and applied to the strapdown inertial navigation system./global positioning system (SINS/GPS) integrated navigation system. Firstly, the maximum entropy robust filtering method is used to deal with the outlier problem caused by the impulse noise, and is effectively dealt with. Then, a posterior update is made with the improved variational Bayesian adaptive method, that estimate the noise and converge the estimated value. Finally, the end of this paper, the simulation results show that the MCVBAKF can effectively improve the filtering accuracy and stability in complex environment.

Key words: maximum correntropy method, robust filtering, adaptive filtering, integrated navigation

CLC Number:

V249.31

Kaixin LUO, Meiping WU, Ying FAN. Robust adaptive filtering based on maximum entropy method and its application[J]. Systems Engineering and Electronics, 2020, 42(3): 667-673.

Figures/Tables 12

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References 31

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算法	姿态误差方差/(′)
算法	纵摇角	横摇角	航向角
KF	2.354	4.861	16.13
MCKF	0.736	3.806	6.474
MCVBAKF	0.430	3.415	5.076

算法	速度误差方差/(m/s)
算法	东向	北向
KF	2.051	4.655
MCKF	2.000	1.578
MCVBAKF	1.337	1.554

算法	位置误差方差/(′)
算法	经度	纬度
KF	0.673	1.062
MCKF	0.697	0.404
MCVBAKF	0.233	0.177

算法	姿态误差方差/(′)
算法	纵摇角	横摇角	航向角
KF	1.651	3.934	7.712
MCKF	0.573	3.652	6.515
MCVBAKF	0.545	3.257	4.512

算法	速度误差方差/(m/s)
算法	东向	北向
KF	2.234	2.509
MCKF	1.780	1.841
MCVBAKF	0.991	1.735

Robust adaptive filtering based on maximum entropy method and its application

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