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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/40704

Title: Fuzzy Adaptive Cubature Kalman Filter for Integrated Navigation Systems
Authors: Chien-Hao Tseng;Sheng-Fuu Lin;Dah-Jing Jwo
Contributors: 國立臺灣海洋大學:通訊與導航工程學系
Date: 2016
Issue Date: 2017-02-06T06:07:31Z
Publisher: Sensor
Abstract: Abstract: 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.
Relation: 12(8)
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/40704
Appears in Collections:[通訊與導航工程學系] 期刊論文

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