Low-cost GPS/INS integration with accurate measurement modeling using an extended state observer

The extended Kalman filter (EKF) is widely used for the integration of the global positioning system (GPS) and inertial navigation system (INS). It is well known that the EKF performance degrades when the system nonlinearity increases or the measurement covariance is not accurate. For the loosely coupled GPS/INS integration, accurate determination of the GPS measurement and its covariance is not a simple task. An extended state observer (ESO) is proposed for the first time to improve the navigation performance of the loosely coupled GPS/INS integration with accurate measurement modeling. The performance of the proposed method is comprehensively evaluated and analyzed, and comparisons were made with respect to the standard EKF method. Simulations and a field vehicular test were conducted to evaluate the performance of the integrated system using the proposed algorithm. The results demonstrate that the navigation performance of the proposed method can be improved significantly in terms of position and velocity when compared with the EKF method. In the simulation test, the RMS values of the positioning errors are reduced by 52.57%, 48.56%, and 34.16% in the north, east, and vertical directions, respectively. The corresponding percentage for the velocity errors are 48.20%, 43.17%, and 22.65%, respectively. In the field test, the RMS values of the positioning errors are decreased by 40.91%, 47.63%, and 12.21%, respectively. The corresponding percentage for the velocity errors are 42.13%, 31.38%, and 33.86%, respectively. The improvement of attitude accuracy is not obvious as it mainly relies on the quality of the inertial sensors.