Track-Consistency-Based GNSS RFI Monitoring Using Crowdsourced ADS-B Sensor Networks

Abstract

Growing reports of global navigation satellite system (GNSS) radio-frequency interference (RFI) highlight the need for scalable wide-area sensing for situational awareness. Crowdsourced Automatic Dependent Surveillance-Broadcast (ADS-B) receiver networks form a large-scale opportunistic sensor network for GNSS RFI monitoring, but ADS-B quality indicators may remain high during abnormal reported-position behavior, and heterogeneous receiver timestamping can produce apparent speed spikes. This letter proposes a three-stage framework that screens position-jump candidates, verifies local track consistency to suppress timing artifacts, and groups confirmed anomalies into traffic-adaptive multi-aircraft events. Using 605 million 1090-MHz ADS-B reports over Northeast Asia from December 2025 to February 2026, the framework identified 166 event clusters within the validity window of Notice to Airmen (NOTAM) RKRR Z1401/25 and none in the pre-NOTAM period. More than 99% of confirmed anomalies remained in high quality-indicator regimes, suggesting that track-consistency verification provides a complementary sensing criterion for GNSS RFI monitoring.