標題: Fuzzy Adaptive Cubature Kalman Filter for Integrated Navigation Systems
作者: Tseng, Chien-Hao
Lin, Sheng-Fuu
Jwo, Dah-Jing
電控工程研究所
Institute of Electrical and Control Engineering
關鍵字: integrated navigation;cubature Kalman filter;unscented Kalman filter;fuzzy logic
公開日期: 八月-2016
摘要: This paper presents a sensor fusion method based on the combination of cubature Kalman filter (CKF) and fuzzy logic adaptive system (FLAS) for the integrated navigation systems, such as the GPS/INS (Global Positioning System/inertial navigation system) integration. The third-degree spherical-radial cubature rule applied in the CKF has been employed to avoid the numerically instability in the system model. In processing navigation integration, the performance of nonlinear filter based estimation of the position and velocity states may severely degrade caused by modeling errors due to dynamics uncertainties of the vehicle. In order to resolve the shortcoming for selecting the process noise covariance through personal experience or numerical simulation, a scheme called the fuzzy adaptive cubature Kalman filter (FACKF) is presented by introducing the FLAS to adjust the weighting factor of the process noise covariance matrix. The FLAS is incorporated into the CKF framework as a mechanism for timely implementing the tuning of process noise covariance matrix based on the information of degree of divergence (DOD) parameter. The proposed FACKF algorithm shows promising accuracy improvement as compared to the extended Kalman filter (EKF), unscented Kalman filter (UKF), and CKF approaches.
URI: http://dx.doi.org/10.3390/s16081167
http://hdl.handle.net/11536/134106
ISSN: 1424-8220
DOI: 10.3390/s16081167
期刊: SENSORS
Volume: 16
Issue: 8
顯示於類別:期刊論文