Abstract
Kalman filters (KF) have been extensively used in the integration of global positioning system (GPS) and inertial navigation system (INS) data. Often, the GPS data is used as a benchmark to update the INS data. In this study, an analysis of integration of GPS data with INS data using an extended Kalman filter is performed in terms of the filter's performance with respect to the amount of noise in the GPS data and the sampling time of the vehicle position. The study further analyzes and compares the pattern of error at varying sampling periods in vehicle trajectories with high curvature path segments and low curvature path segments. Simulation results are presented at the end. The results show that the performance of the KF depends linearly on the amount of noise and sampling times. The relationship between the curvature of the road and the performance of the KF was found to be quadratic.
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