Adaptive-Robust Fusion Strategy for Autonomous Navigation in GNSS-Challenged Environments

High-precision positioning and navigation is highly important for unmanned vehicles in global navigation satellite system (GNSS)-challenged environments. The main aim of this article is to develop an adaptive-robust fusion strategy for low-cost GNSS/ acrlong SINS-integrated systems with aiding information predicted by data predictors, which can provide reliable fusion positioning solutions when the GNSS signal is challenged. For handling the GNSS degraded problem, we make an adaptive-robust modification to the Kalman filter (KF) by introducing a new adaptive factor that can accurately adjust the estimation error covariance matrix and Kalman gain according to the real process. In addition, we design an acrlong SDM network with a broad-deep structure for synthesizing navigation data predictors, in order to struggle with the GNSS denied problem. To testify the effectiveness and robustness of the new fusion algorithm, practical experiments with real data sets gathered from road tests in urban areas have been carried out. The results, that is with more than 80% increase in both north and east direction in GNSS-challenged area of our data sets, show that the proposed adaptive-robust fusion strategy can significantly improve the continuity and reliability of integrated navigation, and offer a more precise, robust, and reliable solution for autonomous navigation in GNSS-challenged environments.