Abstract: In this paper, describes the literature review for design of a “tightly coupled GPS/INS integration system based on nonlinear Kalman filtering methods”. The traditional methods include linearization of the system around a nominal trajectory, and the extended Kalman filtering (EKF) method which linearizes the system around the previous estimate, or the predication, whichever is available. The recently proposed sigma-point Kalman filtering (SPKF) method uses a set of weighted samples (sigma points) to completely capture the first and second order moments of the prior random variable. The project aims to develop a generic hardware/software platform for positioning and imaging sensor integration. In the current development phase, a tightly coupled GPS/INS integration system based on a linearization around the INS solution has been designed and implemented. The system uses the GPS pseudo range and Doppler measurements to estimate the INS errors. This paper describes further developments of the integration filter design based on the EKF and SPKF methodologies, in order to compare the performance of nonlinear filtering approaches.

Keywords: Global positioning system (GPS), inertial navigation system (INS), Non-linear Kalman filter (KF), Computer with latest configuration, Latest version of MATLAB with GPS & INS tool kit.