Variable Gain Robust Estimation Algorithm Based on Quality Evaluation of Measurement Information

Given that satellite signals are susceptible to interference, which causes performance degradation of Kalman estimation, and that the smooth variable structure estimation algorithm has limited applications in integrated navigation, this paper proposes a variable gain robust estimation algorithm based on the quality evaluation of measurements. A measurement quality evaluation index based on the smooth bounded layer is constructed. When the measurements are normal, the Kalman estimation gain is adopted to obtain the optimal estimation. When the measurements are abnormal, the smooth variable structure estimation and the robust adaptive estimation algorithm are combined: for the state quantities corresponding to the measurements, the smooth variable structure estimation gain is used to resist measurement abnormalities; for the state quantities without corresponding measurements, the measurement information evaluation index is used to reconstruct the filtering gain, so as to reduce the impact of abnormal data on the fusion results. The verification results show that under various typical GNSS faults, the proposed algorithm can maintain good estimation accuracy, and can quickly converge to the accuracy under normal conditions after the fault ends, thus improving the robustness of the airborne SINS/GNSS integrated navigation system. Based on the Monte Carlo experimental results, compared with the Kalman filter, the smooth variable structure filter and the innovation-based adaptive Kalman filter, the platform misalignment angle accuracy of the proposed variable gain robust estimation algorithm is improved by more than 10%, the velocity accuracy by more than 26%, and the position accuracy by more than 56%.