A method for detecting faults in integrated navigation system based on improved SPRT adaptive filtering

Abstract

GNSS measurement noise faults can easily cause a drop or even divergence in the filter accuracy of integrated navigation system. Therefore, real-time fault detection and processing are necessary. With the aim of the defects of residual Chi-square test and sequential probability ratio test (SPRT) in detecting measurement noise faults, an adaptive filtering algorithm based on improved SPRT detection is proposed. On the one hand, the influence of the current time innovation on the statistics is strengthened by the method of fading weighting. On the other hand, the measurement update equation of the adaptive filter is constructed by calculating the weight factor of the statistics in real-time, which improves the accuracy and robustness of the state estimation. Simulation experiments show that the proposed algorithm is more sensitive and non-missing compared to the residual Chi-square test in the case of large value abrupt faults in the measurement noise. The proposed algorithm reduces the fault detection delay time by about 65.5% compared to SPRT in the case of slow change faults in measurement noise. In the case of slow change faults with small values, the proposed algorithm reduces the missing detection rate by 40% compared to the residual Chi-square test. In addition, the proposed algorithm is compared with the robust Kalman filter based on the residual Chi-square and Sage-Husa adaptive filtering. The results show that the proposed algorithm has higher navigation accuracy when the system has slow change faults.