Adaptive Estimation of Multiple Fading Factors Based on RGAM Model

When there exists a mismatch between the system model or inaccuracies in the statistical characteristics of the noise, it can directly impact the accuracy of current state estimation and potentially lead to filter divergence. In the domain of dynamic positioning and navigation, the Kalman filter has been proposed as a solution to address these issues by incorporating fading factors. Unlike conventional methods for estimating fading factors, this study introduces the RGAM (Resnet Global Attention Mechanism) algorithm model to estimate multiple fading factors associated with state prediction covariance. The RGAM algorithm model leverages the robust estimation capability of network models to accurately estimate the fading factors corresponding to relevant epochs. To validate the effectiveness of the proposed algorithm, experiments were conducted using an actual road data set within the context of a GNSS/INS (Global Navigation Satellite System/ Internal Navigation System) integrated system. Through these experiments, it was observed that employing a filter with accurate fading factors facilitates the avoidance of filter divergence and achieves stable filtering performance in the integrated system.