Integrated navigation of GPS/INS based on fusion of recursive maximum likelihood IMM and Square-root Cubature Kalman filter

Rui Song; Xiyuan Chen; Yongchun Fang; Haoqian Huang

doi:10.1016/j.isatra.2020.05.049

Integrated navigation of GPS/INS based on fusion of recursive maximum likelihood IMM and Square-root Cubature Kalman filter

ISA Trans. 2020 Oct:105:387-395. doi: 10.1016/j.isatra.2020.05.049. Epub 2020 Jun 2.

Authors

Rui Song¹, Xiyuan Chen², Yongchun Fang³, Haoqian Huang⁴

Affiliations

¹ Institute of Robotics and Automatic Information System, Nankai University, Tianjin 300350, China.
² School of Instrument Science and Engineering, Southeast University, Nanjing 210096, Jiangsu, China.
³ Institute of Robotics and Automatic Information System, Nankai University, Tianjin 300350, China. Electronic address: fangyc@nankai.edu.cn.
⁴ School of Energy and Electrical Engineering, Hohai University, Nanjing 210098, Jiangsu, China.

PMID: 32505341
DOI: 10.1016/j.isatra.2020.05.049

Abstract

Information fusion of the GPS/INS integrated system is always related to characteristics of the inertial system and the sensor feature, yet prior knowledge is still difficult to obtain in real applications. To deal with the uncertainty of error covariance and state noise in vehicle navigation, this paper presents a novel approach, wherein the integration of Square-root Cubature Kalman Filters (SCKF) and Interacting Multiple Model (IMM) are also introduced. In the framework of IMM, the SCKFs with different covariance are designed to reflect various vehicle dynamics. Besides, since the IMM-SCKF can switch flexibly among the filters, the transition probability matrix is computed with maximum likelihood method to adapt to different noise characteristics. The performance of the proposed algorithm is guaranteed by theoretical analyses, and a series of vehicular experiments with different maneuvers are carried out in an urban environment. The results indicate that, in comparison with the CKF and the IMM-CKF, the accuracy of velocity and attitude are increased by the proposed strategy.

Keywords: Cubature Kalman filter; Error estimation; Integrated navigation; Interacting Multiple Model.