Next Article in Journal
Autonomous Underwater Navigation and Optical Mapping in Unknown Natural Environments
Next Article in Special Issue
Stochastic Modeling and Analysis of Multiple Nonlinear Accelerated Degradation Processes through Information Fusion
Previous Article in Journal
Recent Progress on Cellulose-Based Electro-Active Paper, Its Hybrid Nanocomposites and Applications
Previous Article in Special Issue
State Estimation for a Class of Non-Uniform Sampling Systems with Missing Measurements
Open AccessArticle

Fuzzy Adaptive Cubature Kalman Filter for Integrated Navigation Systems

Institute of Electrical Control Engineering, National Chiao Tung University, Hsinchu 300, Taiwan
Department of Communications, Navigation and Control Engineering, National Taiwan Ocean University, Keelung 202, Taiwan
Author to whom correspondence should be addressed.
Academic Editor: Leonhard M. Reindl
Sensors 2016, 16(8), 1167;
Received: 13 April 2016 / Revised: 28 June 2016 / Accepted: 16 July 2016 / Published: 26 July 2016
(This article belongs to the Special Issue Advances in Multi-Sensor Information Fusion: Theory and Applications)
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. View Full-Text
Keywords: integrated navigation; cubature Kalman filter; unscented Kalman filter; fuzzy logic integrated navigation; cubature Kalman filter; unscented Kalman filter; fuzzy logic
Show Figures

Figure 1

MDPI and ACS Style

Tseng, C.-H.; Lin, S.-F.; Jwo, D.-J. Fuzzy Adaptive Cubature Kalman Filter for Integrated Navigation Systems. Sensors 2016, 16, 1167.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop