Title :
Fuzzy Adaptive Unscented Kalman Filter for Ultra-Tight GPS/INS Integration
Author :
Jwo, Dah-Jing ; Chung, Fong-Chi
Author_Institution :
Dept. of Commun., Navig. & Control Eng., Nat. Taiwan Ocean Univ., Keelung, Taiwan
Abstract :
This paper presents a sensor fusion method based on the combination of adaptive unscented Kalman filter (UKF) and Fuzzy Logic Adaptive System (FLAS) for the ultra-tightly coupled GPS/INS integrated navigation. The UKF employs a set of sigma points by deterministic sampling, such that the linearization process is not necessary, and therefore the error caused by linearization as in the traditional extended Kalman filter (EKF) can be avoided. The adaptive algorithm has been one of the approaches to prevent divergence problem of the filter when precise knowledge on the system models are not available. Through the use of fuzzy logic, the FLAS has been incorporated into the AUKF as a mechanism for timely detecting the dynamical changes and implementing the on-line tuning of the factors in the weighted covariance matrices by monitoring the innovation information so as to maintain good estimation accuracy and tracking capability. The performance assessment for UKF and FUKF are carried out.
Keywords :
Global Positioning System; adaptive Kalman filters; covariance matrices; integration; sensor fusion; EKF; FLAS; UKF; covariance matrices; estimation accuracy; extended Kalman filter; fuzzy adaptive unscented Kalman filter; sensor fusion method; tracking capability; ultra-tight GPS-INS integration; Adaptive filter; Fuzzy logic; Ultra-tight integration; Unscented Kalman filter;
Conference_Titel :
Computational Intelligence and Design (ISCID), 2010 International Symposium on
Conference_Location :
Hangzhou
Print_ISBN :
978-1-4244-8094-4
DOI :
10.1109/ISCID.2010.148