Fuzzy Logic-Based Adaptive Filtering for Transfer Alignment

The transfer alignment of strapdown inertial navigation systems (SINSs) is of great significance for improving the strike accuracy of airborne tactical vehicles. This study designed a new fuzzy logic-based adaptive filtering method by using the fuzzy logic theory to address the influence of system model error on the state estimation of the Kalman filter for SINS transfer alignment. It established the state error model and measurement error model, which were embedded with the state prediction residual and measurement residual, respectively, for SINS transfer alignment. The fuzzy rules were designed and introduced into the Kalman filtering framework to estimate the covariances of the system measurement and predicted state by minimizing their residuals to improve filtering accuracy for SINS transfer alignment. Simulation and experimentation together with associated comparative analyses were conducted, demonstrating that the proposed method can effectively handle the influence of system model error on SINS transfer alignment, and its accuracy is at least 18.83% higher than benchmark methods for transfer alignment.