Cubature Kalman Filter Under Minimum Error Entropy With Fiducial Points for INS/GPS Integration

• Published in: IEEE/CAA journal of automatica sinica Vol. 9; no. 3; pp. 450 - 465
• Main Authors: Dang, Lujuan, Chen, Badong, Huang, Yulong, Zhang, Yonggang, Zhao, Haiquan
• Format: Journal Article
• Language: English
• Published: Piscataway Chinese Association of Automation (CAA) 01.03.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); 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
• Subjects: Complexity; Computational complexity; Convergence; Cubature Kalman filter (CKF); Entropy; Errors; Global positioning systems; GPS; Inertial navigation; inertial navigation system (INS)/global positioning system (GPS) integration; Kalman filters; minimum error entropy with fiducial points (MEEF); Navigation systems; non-Gaussian noise; Optimization; Parameters; Regression models; Robustness; Robustness (mathematics); Satellite navigation systems; State estimation; Target tracking; Tracking; minimum error entropy with fiducial points (MEEF); Cubature Kalman filter (CKF); non-Gaussian noise; inertial navigation system (INS)/global positioning system (GPS) integration
• DOI: 10.1109/JAS.2021.1004350
• ISSN: 2329-9266

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.