# Data Fusion Based on Adaptive Interacting Multiple Model for GPS/INS Integrated Navigation System
## 1 Introduction  
In general, the GPS/INS integrated system is combined with differential GPS in order to achieve higher accuracy [1]. This is basically because the inherent characteristics of high-precision differential GPS are better than the independent GPS mode. It is regrettable that this needs the presence of a correspondingly close base station, which not only restricts the scope of navigation but also spends more on construction of the base station. If GPS is combined with INS sensor, the former has the ability to give data about position and speed precisely, yet the latter has the capability to output data of attitude reliably. The integrated navigation system thus can conquer the drawbacks one another, while keeping the high-accuracy information. If an inertial navigation system (INS) is integrated with GPS, GPS is mainly utilized for offsetting the accumulation errors of speed and position caused by INS, and the INS provides a better solution in the process of GPS signal unavailability [2; 3]. Under the condition that the number of the available satellites rises to four and the influence of measurement noise is inconspicuous, credible solutions can be provided by GPS. This may be a significant flaw if GPS works independently. In general, three methods are used to restrain the problem, the first is exploiting INS with higher accuracy, the second is adding some accessory device, and the last is adopting advanced techniques and methods. In this thesis, the last two methods are united to ameliorate the better navigation solution. The EKF [4], which is regarded as the most advanced method of integrating INS and GPS data, has been widely used for information fusion algorithms [5; 6; 7; 8]. Under the condition that the noise is deemed as Gaussian, the nonlinear state and observation equations should be linearized according to the first-order Taylor series expansion. Nevertheless, higher order terms are ignored in the calculation process so EKF may not generate valid estimation of solutions, particularly when GPS interruption. Especially for low-cost MEMS-based inertial measurement units (IMU), this case is even more common. The unscented Kalman filter (UKF) [9] was introduced as a linear regression estimation filter. In order to achieve the posterior mean and covariance precisely, UKF multiplies a deterministically group of sampling points which has suitable weights through the non-linear dynamic models and measurement models in the matter of any nonlinearity. It is very difficult to overcome weak points of assumption of Gaussian distribution to be tackled [10; 11; 12]. However, the capability of these improved KFs hinge upon the system being considered, and if the system has stronger nonlinear action, the accuracy of mode estimation will be poor. In the period of GPS signal unavailability, the navigation information based on KF diverges owing to process of linearization and inferior system model [13].To address the shortcomings of KF-based methods mentioned above, many valuable efforts have been applied. A common approach for adjustment of covariance matrix on-line called innovation adaptive estimation can be used to solve the unstability of KF-based methods, and then innovation adaptive estimation adaptive Kalman filtering (IAEAKF) [14; 15; 16] based on memory attenuation is present to prevent filtering divergence and reduce the influence of ambient noise. Also, residual sequences are applied to strengthen the random characteristics of filter on line. In order to enhance the estimation precision, adaptive filtering with fading memory algorithm is investigated. A new data fusion method is proposed to lessen risk of integrated vehicle health maintenance system (IVHMS). The purpose of complicated matrix operation in IVHMS is designed to dominate the time of high-order systems, which can be reduced by employing fuzzy Kalman filter (FKF). FKF is used to regulate performance. In terms of lessening IVHMS risk, this algorithm is a valid technique [17]. As an alternative, multiple model (MM) estimation was introduced.