期刊
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
卷 9, 期 3, 页码 450-465出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JAS.2021.1004350
关键词
Cubature Kalman filter (CKF); inertial navigation system (INS)/global positioning system (GPS) integration; minimum error entropy with fiducial points (MEEF); non-Gaussian noise
资金
- Fundamental Research Funds for the Central Universities [xzy022020045]
- National Natural Science Foundation of China [61976175]
The paper proposes a robust nonlinear Kalman filter called MEEF-CKF, which exhibits strong robustness against complex non-Gaussian noises. It addresses the issue of biased estimates in the traditional CKF when the INS/GPS system faces non-Gaussian disturbances. The MEEF-CKF operates major steps including regression model construction, robust state estimation, and free parameters optimization for enhanced robustness.
Traditional cubature Kalman filter (CKF) is a preferable tool for the inertial navigation system (INS)/global positioning system (GPS) integration under Gaussian noises. The CKF, however, may provide a significantly biased estimate when the INS/GPS system suffers from complex non-Gaussian disturbances. To address this issue, a robust nonlinear Kalman filter referred to as cubature Kalman filter under minimum error entropy with fiducial points (MEEF-CKF) is proposed. The MEEF-CKF behaves a strong robustness against complex non-Gaussian noises by operating several major steps, i.e., regression model construction, robust state estimation and free parameters optimization. More concretely, a regression model is constructed with the consideration of residual error caused by linearizing a nonlinear function at the first step. The MEEF-CKF is then developed by solving an optimization problem based on minimum error entropy with fiducial points (MEEF) under the framework of the regression model. In the MEEF-CKF, a novel optimization approach is provided for the purpose of determining free parameters adaptively. In addition, the computational complexity and convergence analyses of the MEEF-CKF are conducted for demonstrating the calculational burden and convergence characteristic. The enhanced robustness of the MEEF-CKF is demonstrated by Monte Carlo simulations on the application of a target tracking with INS/GPS integration under complex non-Gaussian noises.
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