Robust cubature Kalman filter for GNSS/INS with missing observations and colored measurement noise

Bingbo Cui; Xiyuan Chen; Xihua Tang; Haoqian Huang; Xiao Liu

doi:10.1016/j.isatra.2017.09.019

Robust cubature Kalman filter for GNSS/INS with missing observations and colored measurement noise

ISA Trans. 2018 Jan:72:138-146. doi: 10.1016/j.isatra.2017.09.019. Epub 2017 Oct 10.

Authors

Bingbo Cui¹, Xiyuan Chen², Xihua Tang¹, Haoqian Huang¹, Xiao Liu¹

Affiliations

¹ School of Instrument Science and Engineering, Southeast University, Nanjing, Jiangsu 210096, China; Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology Ministry of Education, Nanjing, Jiangsu 210096, China.
² School of Instrument Science and Engineering, Southeast University, Nanjing, Jiangsu 210096, China; Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology Ministry of Education, Nanjing, Jiangsu 210096, China. Electronic address: chxiyuan@seu.edu.cn.

PMID: 29029796
DOI: 10.1016/j.isatra.2017.09.019

Abstract

In order to improve the accuracy of GNSS/INS working in GNSS-denied environment, a robust cubature Kalman filter (RCKF) is developed by considering colored measurement noise and missing observations. First, an improved cubature Kalman filter (CKF) is derived by considering colored measurement noise, where the time-differencing approach is applied to yield new observations. Then, after analyzing the disadvantages of existing methods, the measurement augment in processing colored noise is translated into processing the uncertainties of CKF, and new sigma point update framework is utilized to account for the bounded model uncertainties. By reusing the diffused sigma points and approximation residual in the prediction stage of CKF, the RCKF is developed and its error performance is analyzed theoretically. Results of numerical experiment and field test reveal that RCKF is more robust than CKF and extended Kalman filter (EKF), and compared with EKF, the heading error of land vehicle is reduced by about 72.4%.

Keywords: Colored noise; Fiber optic gyro; Integrated navigation; Robust cubature Kalman filter.