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.

Lujuan Dang;Badong Chen;Yulong Huang;Yonggang Zhang;Haiquan Zhao

Institute of Artificial Intelligence and Robotics,Xi'an Jiaotong University,Xi'an 710049,China;Department of Automation,Harbin Engineering University,Harbin 150001,China;School of Electrical Engineering,Southwest Jiao-tong University,Chengdu 610000,China

自动化学报（英文版）

[1]Lujuan Dang;Badong Chen;Yulong Huang;Yonggang Zhang;Haiquan Zhao-.Cubature Kalman Filter Under Minimum Error Entropy With Fiducial Points for INS/GPS Integration)[J].自动化学报（英文版）,2022(03):450-465

MEEF,linearizing,calculational

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