Enhanced Fault Detection for GNSS/INS Integration Using Maximum Correntropy Filter and Local Outlier Factor

Fault detection is crucial to isolate positioning risks for safety-critical applications using Global Navigation Satellite Systems. Conventional Kalman filter-based fault detection methods mainly focus on satellite measurement faults and presume that the test statistics follow the chi-square distribution. These methods ignore the adverse effect of undetected faults occurring previously, and the detection performance would be restrained under a mis-matched distribution assumption. To solve these issues, an enhanced fault detection method is proposed in this paper, which combines the Maximum Correntropy Criterion (MCC) and Local Outlier Factor (LOF). The MCC is introduced to derive a robust extended Kalman filter to deal with the undetected faults. Simultaneously, a specific Kernel Bandwidth (KB) for each measurement is calculated by the innovation and innovation covariance matrix to handle the inherent restriction of a fixed KB. Moreover, the LOF is used to reconstruct the test statistics, and the threshold is calculated by an offline model. Simulations are conducted to evaluate the proposed method under different fault scenarios. The results illustrate that the adaptive robust estimation reduces the negative influence of undetected faults, which makes the filter innovation follow the actual fault amplitudes. The proposed algorithm effectively improves the fault detection rate and positioning accuracy.