Detection and mitigation of multipath effects in GPS signals from low-elevation satellites using SVMD and PSD-entropy approach

Multipath is a significant cause of inaccuracy in Global Navigation Satellite System (GNSS) signal processing, reducing the accuracy and dependability of navigation solutions. This paper provides a novel framework for multipath mitigation in GNSS signals that combines Successive Variational Mode Decomposition (SVMD) with feature-based mode selection, utilising the Power Spectral Density (PSD)-Entropy Approach. The GNSS Multipath Analysis Software tool determines the multipath errors on the GPS constellation on L1 and L2 frequencies. The GNSS receiver data for this analysis were collected on November 10, 2024, from the IISC Bangalore GNSS station, India, with a latitude of 13.0212 and a longitude of 77.570. The multipath parameters, MP1 and MP2, were computed using RINEX version 2 observation and navigation data files for the GPS constellation. SVMD decomposes these multipath signals into modes, and the power spectral density (PSD) with Shannon entropy is utilised to identify multipath-affected modes. By eliminating the multipath-affected modes, the signal is reconstructed using the remaining modes, and the approaches’ performance was measured using standard deviation (STD), variance, and RMS value as metrics. The PSD-Entropy technique generates a more minor STD than the Hilbert Transform, resulting in a more precise multipath effect reduction and outperforming traditional methods. The SVMD with the PSD-Entropy method is a data-driven approach that identifies multipath-dominant modes through an assessment of spectral information and entropy evaluation, providing an advantage over fixed-threshold methods in selecting multipath-affected modes. Advanced signal decomposition methods prove useful for GNSS multipath mitigation through this study while creating research foundations for multi-constellation analysis.