Radio-frequency interference location, detection and classification using deep neural networks
Document typeConference report
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Rights accessOpen Access
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.
CitationPérez, A. [et al.]. Radio-frequency interference location, detection and classification using deep neural networks. A: IEEE International Geoscience and Remote Sensing Symposium. "IGARSS 2020: 2020 IEEE International Geoscience and Remote Sensing Symposium Proceedings: September 26 - October 2, 2020: virtual symposium". Institute of Electrical and Electronics Engineers (IEEE), 2020, p. 6977-6980. ISBN 978-1-7281-6374-1. DOI 10.1109/IGARSS39084.2020.9324532.
- Departament de Física - Ponències/Comunicacions de congressos 
- RSLAB - Remote Sensing Research Group - Ponències/Comunicacions de congressos 
- Doctorat en Teoria del Senyal i Comunicacions - Ponències/Comunicacions de congressos 
- Departament de Teoria del Senyal i Comunicacions - Ponències/Comunicacions de congressos [3.090]
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