GPS/INS Integration Using Sigma Point Kalman Filter

The accuracy of Global Positioning System (GPS) is increased with the coupling to Inertial Navigation System (INS) to accomplish navigation. This paper proposes an algorithm to filter and fuse the GPS and INS information. Sigma point kalman filter is employed to simulate the information convergence of the dynamic model which maintains better performance in nonlinear system. So we can obtain a better precise filtering result when both are online. At the same time, the INS data is trained with the result as training target when it is the unique input. This paper raises the concept that Support Vector Machine (SVM) is adopted to train the INS data when GPS is offline and the simulated annealing is applied to realize the optimization of the parameters of kernel function and the penalty function in the SVM algorithm. Therefore, the integration navigation could retain almost as precise as the GPS when the GPS is out of coverage.