Radio-frequency interference location, detection and classification using deep neural networks
Abstract
Global Navigation Satellite System (GNSS) signals are used in Earth Observation for Radio Occultation and Reflectometry. The increasing effects of Radio-Frequency Interferences (RFI) on the performance of these receivers and navigation have suddenly sparked serious concerns due to their proliferation. Detection and mitigation of RFI heavily relies on the nature and location of the interfering sources. In some cases, null-steering or shielding can be used to mitigate RFI effects. In this work, a system to detect and locate RFI sources is presented, including signal classification and recording for countermeasure-related decision-making.
Type
Publication
IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium
Abstract & Overview
Global Navigation Satellite System (GNSS) signals are used in Earth Observation for Radio Occultation and Reflectometry. The increasing effects of Radio-Frequency Interferences (RFI) on the performance of these receivers and navigation have suddenly sparked serious concerns due to their proliferation. Detection and mitigation of RFI heavily relies on the nature and location of the interfering sources. In some cases, null-steering or shielding can be used to mitigate RFI effects. In this work, a system to detect and locate RFI sources is presented, including signal classification and recording for countermeasure-related decision-making.
Publication Metadata
- Authors: A. Perez, Jorge Querol, H. Park, A. Camps
- Venue: IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium (2020)
- Research Areas: RFI Mitigation & GNSS Security, AI & Neuromorphic SatCom
- DOI: 10.1109/igarss39084.2020.9324532