## 1 Introduction
The IMM estimator obtains its estimate as a weighted sum of the individual estimates from a number of parallel filters matched to different motion modes of the platform. The objective is to design the nonlinear filter in an IMM algorithm suitable for high dynamic or curvilinear motions to navigate a maneuvering vehicle. Selected results presented in this paper confirm the improvements. The IMM algorithm has been originally applied to target tracking [14-17], and recently extended to Global Positioning System (GPS) navigation [18,19], and integrated navigation designs [20-23]. A model probability evaluator calculates the current probability of the vehicle being in each of the possible modes. A global estimate of the vehicle's state is computed using the latest mode probabilities. This algorithm carries out a soft-switching between the various modes by adjusting the probabilities of each mode, which are used as weightings in the combined global state estimate. The covariance matrix associated with this combined estimate takes into account the covariances of the mode-conditioned estimates as well as the differences between these estimates. The UKF naturally suffers the same problem as the EKF. The uncertainty of the process noise and measurement noise will degrade the performance of the UKF. An adaptive mechanism which dynamically identifies uncertainties or modeling errors can be adopted. To deal with the noise uncertainty and system nonlinearity simultaneously, the IMMUKF can be introduced [24,25]. In the approach, these multiple models are developed to describe various dynamic behaviors. In each model an UKF is running, and the IMM algorithm makes uses of model probabilities to weight the inputs and output of a bank of parallel filters at each time instant. The fuzzy logic reasoning system is based on the Takagi-Sugeno (T-S) model. The fuzzy reasoning system is constructed for obtaining the suitable process noise according to the time-varying change in dynamics. By monitoring the innovation information, the Fuzzy logic adaptive system (FLAS) is employed for dynamically on-line determining better lower and upper bounds of the process noise covariance according to the innovation information, and therefore improves the estimation performance. The synergy of Global Positioning System (GPS) and inertial navigation system (INS) has been widely explored due to their complementary operational characteristics [1,26]. The GPS/INS integrated navigation system, typically carried out through the EKF and UKF, is the adequate solution to provide a navigation system that has the superior performance in comparison with either the GPS or INS stand-alone system. The example on the tightly-coupled GPS/INS integrated navigation processing based on the FUZZY-IMMUKF will be presented. The performance comparison will be demonstrated by using the proposed FUZZY-IMMUKF method as compared to the relatively conventional UKF and IMMUKF approaches. The remainder of this paper is organized as follows. In Section 2, the preliminary background on the interacting multiple model unscented Kalman filter for the navigation processing is discussed. The proposed sensor fusion strategy is introduced in Section 3. In Section 4, the navigation integration processing and performance evaluation are carried out to evaluate the performance comparison will be demonstrated using the proposed FUZZY-IMMUKF method as compared to the relatively conventional UKF and IMMUKF approaches. Conclusions are given in Section 5.