Automatic RFI Detection, Location, and Classification System in GNSS Bands

Global Navigation Satellite Systems (GNSS) are critical infrastructure components in modern Positioning, Navigation, and Timing (PNT) services, playing a vital role in both civilian and defense applications. These systems operate in specific frequency bands that are also utilized by other Earth Observation technologies, such as GNSS-Radio Occultations and GNSS-Reflectometry. Other passive microwave remote sensing techniques such as microwave radiometers, work with very faint signals in nearby frequency bands within the L-Band. However, the increasing prevalence of Radio-Frequency Interferences (RFI) poses a significant threat, potentially compromising the integrity and reliability of PNT services, and corrupting geophysical observations. Effective RFI mitigation relies on accurate detection and classification of interference sources, a task that becomes increasingly challenging due to the complexity and diversity of RFI signals. This work presents an automated classification system for RFI detection and characterization in GNSS bands. The methodology employs advanced digital signal processing techniques and statistical algorithms to improve RFI detection and classification. RFI events are then stored in a long-term database to provide insights into the local spectrum, and to aid in mitigation and law enforcement efforts. This study provides a description of the classification system, including its architecture, implementation, and performance analysis. The results highlight the potential of this system to enhance the resilience of GNSS PNT services against RFI.