Error Correction by AI in High-precision Navigation Systems

This research thoroughly examines the inaccuracies resulting from ionospheric and tropospheric delays, as well as multipath effects, in the high-precision positioning of the Global Navigation Satellite System (GNSS). At the same time, the limitations of traditional correction methods (such as Klobuchar model, global ionospheric map and Hopfield model) and differential GNSS, RTK and PPP technologies in practical applications are also discussed. In order to solve the problem that the traditional method depends on the reference station and is greatly affected by occlusion, this paper introduces a novel approach to error correction utilizing artificial intelligence techniques. Experimental results show that in complex urban scenes, the AI method can improve the positioning accuracy by about 20% to 30%, and effectively reduce the error caused by multipath effects and atmospheric delay. In addition, we point out the challenges of data dependence, insufficient model generalization ability and high computational complexity, and propose future development directions such as multi-source data fusion, lightweight model design and cloud-edge collaboration. These provide theoretical and practical support for the fields of automatic driving, UAV navigation and precision mapping.