Fuzzy Adaptive Unscented Kalman Filter for Ultra-Tight GPS/INS Integration

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.