(Peer-Reviewed) Cubature Kalman Filter Under Minimum Error Entropy With Fiducial Points for INS/GPS Integration
Lujuan Dang ¹, Badong Chen 陈霸东 ¹, Yulong Huang 黄玉龙 ², Yonggang Zhang 张勇刚 ², Haiquan Zhao 赵海全 ³
¹ Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, China
中国 西安 西安交通大学 人工智能与机器人研究所
² Harbin Engineering University, Harbin, China
中国 哈尔滨 哈尔滨工程大学
³ School of Electrical Engineering, Southwest Jiaotong University, Chengdu, China
中国 成都 西南交通大学电气工程学院
Abstract
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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