Fuzzy adaptive interacting multiple model unscented Kalman filter for integrated navigation

Abstract

In this paper, application of fuzzy interacting multiple model unscented Kalman filter (FUZZY-IMMUKF) approach to integrated navigation processing for maneuvering vehicle is presented. The unscented Kalman filter (UKF) employs a set of sigma points through 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. Fuzzy logic adaptive system (FLAS) is employed to determine the lower and upper bounds of the system noise through fuzzy inference system (FIS). The use of interacting multiple model (IMM), which describes a set of switching models, finally provides the suitable value of process noise covariance. Consequently, the resulting sensor fusion strategy can efficiently deal with the nonlinear problem in vehicle navigation. The proposed FUZZY-IMMUKF algorithm shows significant improvement in navigation estimation accuracy as compared to the UKF and interacting multiple model unscented Kalman filter (IMMUKF) approaches.