An Unexpected Challenge in GNSS-Reflectometry: Experimental Investigation of EMI From LED Street Lighting on Signal Quality
Radio Science, vol.61, no.6, 2026 (SCI-Expanded, Scopus)
- Publication Type: Article / Article
- Volume: 61 Issue: 6
- Publication Date: 2026
- Doi Number: 10.1029/2026rs008603
- Journal Name: Radio Science
- Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Compendex, INSPEC, zbMATH, Natural Science Collection (ProQuest), Earth, Atmospheric, & Aquatic Science Collection (ProQuest), Technology Collection (ProQuest)
- Keywords: double ridge horn antenna, EMI, GNSS-reflectometry (GNSS-R), LED lighting
- Karadeniz Technical University Affiliated: Yes
Abstract
Global Navigation Satellite Systems (GNSS) are crucial for positioning and increasingly valuable for remote sensing via GNSS-Reflectometry (GNSS-R), which utilizes signals reflected from the Earth's surface to monitor parameters like sea level, soil moisture, and snow depth. This low-cost, passive method provides continuous data, though its accuracy depends heavily on antenna characteristics and electromagnetic environmental conditions. In this study, a 3D-printed dual-ridged horn antenna with gains of 9 dB (L1 band) and 8 dB (L2 band) was used in a 24-hr measurement campaign. Surprisingly, a nearby LED streetlight induced significant electromagnetic interference (EMI) during nighttime operation. Active lighting periods resulted in dramatically reduced Signal-to-Noise Ratio (SNR), frequent signal dropouts, and distorted skyview, with spectrum analyzer measurements confirming that high-frequency switching of LED driver circuits causes electromagnetic interference in the GNSS L1 band. Average SNR dropped from 42 to 28 dB, sometimes falling below 25 dB, while morning SNR values exhibited recovery upon deactivation of the LED system. To mitigate the impact of these signal anomalies, a statistical jump detection mechanism was implemented to identify and flag EMI-induced transitions. This study highlights the vulnerability of GNSS-R stations to urban EMI and introduces an automated detection approach to ensure data integrity in affected environments.