Modified Gaussian Sum Filtering Methods for INS/GPS Integration

Abstract

In INS (Inertial Navigation System) /GPS (Global Positioning System) integration, nonlinear models should be properly handled. The most popular and commonly used method is the Extended Kalman Filter (EKF) which approximates the nonlinear state and measurement equations using the first order Taylor series expansion. On the other hand, recently, some nonlinear filtering methods such as Gaussian Sum filter, particle filter and unscented Kalman filter have been applied to the integrated systems. In this paper, we propose a modified Gaussian Sum filtering method and apply it to land-vehicle INS/GPS integrated navigation as well as the in-motion alignment systems. The modification of Gaussian Sum filter is based on a combination of Gaussian Sum filter and so-called unscented transformation which is utilized in the unscented Kalman filter in order to improve the treatment of the nonlinearity in Gaussian Sum filter. In this paper, the performance of modified Gaussian Sum filter based integrated systems is compared with other filters in numerical simulations. From simulation results, it was found that the proposed filter can improve transient responses of the filter under large initial estimation errors.