An optimized fusion algorithm for an inertial-based integrated navigation system under fault

In inertial-based integrated navigation systems, the outputs of auxiliary systems are often affected by faults. However, the traditional fault diagnosis algorithms that isolated faulty subsystems or measurements were weak for small-amplitude or slow-changing faults which were caused by disturbed environments, which essentially introduced faults into the navigation results. To improve the accuracy of the inertial-based integrated navigation system under disturbance faults, an optimized algorithm for multi-sensor fusion is proposed. The fault statistic of the aided navigation information is calculated using the state chi-square test. Then, a fault-threshold ratio is constructed by combining the detection threshold and the lower bound. The fault-threshold ratios are normalized by integrating the fault analysis of all subsystems. The results are utilized as the coefficients for sub-filters, weighting the variance of state estimation after a measurement update. Finally, the algorithm is tested offline in a street scenario. Experimental results show that the proposed algorithm can reduce the positioning error mean by 75.7% compared with the none-detection mode.