Multipath Identification and Mitigation for Enhanced GNSS Positioning in Urban Environments

Area of Science:

• Geomatics Engineering
• Signal Processing

Background:

• Urban Global Navigation Satellite System (GNSS) positioning faces challenges from multipath reflections and interference.
• Accurate location-based services (LBS) in metropolitan areas require robust solutions to these GNSS signal errors.

Purpose of the Study:

• To develop and validate an adaptive, data-driven methodology for identifying and mitigating GNSS multipath effects in urban environments.
• To improve the accuracy and reliability of GNSS positioning for LBS in challenging urban settings.

Main Methods:

• Utilized Fourier transform (FT) to analyze code multipath and other error sources in the frequency domain via power spectrum.
• Applied wavelet decomposition and signal spectrum analysis to detect multipath signatures in multilayer signal decompositions.
• Developed a mitigation strategy based on identified multipath features to reconstruct corrected GNSS observations.

Main Results:

• Successfully identified and characterized multipath effects in GNSS observations using frequency and decomposition techniques.
• Demonstrated effective mitigation of multipath impacts, leading to improved positioning accuracy.
• Validated the proposed method in both static and dynamic scenarios under realistic urban conditions.

Conclusions:

• The proposed adaptive, data-driven approach offers a viable alternative to complex modeling for GNSS multipath mitigation in urban areas.
• The method integrates seamlessly with existing positioning models and shows feasibility with standard navigation terminals and smartphones.
• This research contributes to enhancing the robustness of GNSS positioning for LBS in challenging urban environments.