Performance Improvement of GPS/INS Integrated System Using Allan Variance Analysis

Because of the sensor noises, the accuracy of a low cost INS deteriorates very rapidly with time. In the INS, deterministic noises of inertial sensors are easing compensated, but random noises must be compensated using a Kalman filter. In this paper, an accurate random noise model of a MEMS inertial sensor is derived to improve the performance of a low cost INS integrated system. The Allan variance analysis method is adopted to model the inertial sensor random noise and then random noise sources are identified and modelled to design the improved model of low cost INS integrated system. If the noise model is accurate, then the navigation performance of newly designed GPS/INS system shows better performance during a GPS blockage. In order to verify the navigation performance of the newly designed GPS/INS system, a car navigation test has been carried out in this research. The test results show a better performance of the improved GPS/INS system model than that of the old GPS/INS system without the Allan variance analysis