## 1 Introduction  
A navigation filter is commonly designed by use of an extended Kalman filter (EKF) [1-3] to estimate the vehicle state variables and suppress the navigation measurement noise. Although it has been shown to be a minimum mean square error estimator, the fact that EKF highly depends on a predefined dynamics model forms a major drawback. For achieving good filtering results, the designers are required to have the complete _a priori_ knowledge on both the dynamic process and measurement models, in addition to the assumption that both the process and measurement are corrupted by zero-mean Gaussian white sequences. As a deterministic sampling approach, the unscented Kalman filter (UKF) [4-9] was first proposed by [PERSON], _et al_. [4] to address nonlinear state estimation in the context of control theory. The UKF is a nonlinear, distribution approximation method, which uses a finite number of carefully chosen sigma points to propagate the probability of state distribution through the nonlinear dynamics of system so as to completely capture the true mean and covariance of the Gaussian random variable (GRV) with a minimal set of samples. The UKF made a Gaussian approximation with a limited number sigma points by using the Unscented Transform (UT). The basic premise behind the UKF is it is easier to approximate a Gaussian distribution than it is to approximate an arbitrary nonlinear function. When the sample points are propagated through the true nonlinear system, the posterior mean and covariance can be captured accurately to the second order of Taylor series expansion for any nonlinear system. One of the remarkable merits is that the overall computational complexity of the UKF is the same as that of the EKF [8]. A high gain (high bandwidth) filter is needed to respond fast enough to the platform maneuvers while a low gain filter is necessary to reduce the estimation errors during the uniform motion periods. Under various circumstances where there are uncertainties in the system model and noise description, and the assumptions on the statistics of disturbances are violated due to the fact that in a number of practical situations, the availability of a precisely known model is unrealistic. One way to take them into account is to consider a nominal model affected by uncertainty. An a parametric adaptation approach, the adaptive Kalman filter (AKF) algorithm has been one of the strategies considered for estimating the state vector to prevent divergence problem due to modeling errors [9-11]. Many efforts have been made to improve the estimation of the covariance matrices based on the innovation-based estimation approach, resulting in the innovation adaptive estimation (IAE) [2,10,11]. Two popular types of the adaptive Kalman filter algorithms include the innovation-based adaptive estimation (IAE) approach [10,11] and the adaptive fading memory filter approach, which is a type of covariance scaling method. One of the adaptive fading memory filters is called the strong tracking Kalman filter [9], where the strong tracking algorithm (STA) involves a nonlinear smoother algorithm that employs suboptimal multiple fading factors. The other major approach that has been proposed for AKF is the multiple model adaptive estimate (MMAE). An a structural adaptation approach, the interacting multiple model (IMM) algorithm [3,12,13] has the configuration that runs in parallel several model-matched state estimation filters, which exchange information (interact) at each sampling time. The IMM approach is based on filter structural adaptation (model switching). Based on a soft-switching framework, the IMM algorithm allows the possibility of using highly dynamic models just when required, diminishing unrealistic noise considerations in non-maneuvering situations of the system. The use of an IMM allows exploiting the benefits of high dynamic models in the problem of vehicle navigation.