GNSS Carrier-Phase Multipath Modeling and Correction: A Review and Prospect of Data Processing Methods

Abstract

A multipath error is one of the main sources of GNSS positioning errors. It cannot be eliminated by forming double-difference and other methods, and it has become an issue in GNSS positioning error processing, because it is mainly related to the surrounding environment of the station. To address multipath errors, three main mitigation strategies are employed: site selection, hardware enhancements, and data processing. Among these, data processing methods have been a focal point of research due to their cost-effectiveness, impressive performance, and widespread applicability. This paper focuses on the review of data processing mitigation methods for GNSS carrier-phase multipath errors. The paper begins by elucidating the origins and mitigation strategies of multipath errors. Subsequently, it reviews the current research status pertaining to data processing methods using stochastic and functional models to counter multipath errors. The paper also provides an overview of filtering techniques for extracting multipath error models from coordinate sequences or observations. Additionally, it introduces the evolution and algorithmic workflow of sidereal filtering (SF) and multipath hemispherical mapping (MHM), from both coordinate and observation domain perspectives. Furthermore, the paper emphasizes the practical significance and research relevance of multipath error processing. It concludes by delineating future research directions in the realm of multipath error mitigation.

Deep Learning CNN-GRU Method for GNSS Deformation Monitoring Prediction

“…It provides real-time and high-precision deformations, facilitating effective analysis and predictions. However, the complex environment around hydraulic structures leads to measurement errors, such as atmospheric delays [11][12][13] and multipath effects [14][15][16][17]. These errors are difficult to process, which adds obstacles to the prediction Appl.…”