Computer Science Applications; Earth and Planetary Sciences (all)
Abstract :
[en] Passive Microwave remote sensing is a very energy-efficient and time-proven technology for remote sensing. Microwave Radiometry and Global Navigation Satellite System (GNSS)-Reflectometry are two widely-used examples. The increasing effects of Radio-Frequency Interferences (RFI) on the performance of these receivers have sparked serious concerns due to their proliferation. Detection and mitigation of RFI heavily rely on the identification and location of the interference sources. Due to the vast amount of degrees of freedom found in these RFIs, classification using classical signal processing techniques becomes problematic, especially if combined RFIs are found within the same bandwidth. In this work, continuous recordings of real RFI events from a real-time system to detect and locate RFI sources are used as training data for an automated classification system, the description and performance of which are presented and analyzed.
Disciplines :
Electrical & electronics engineering
Author, co-author :
Perez-Portero, Adrian; Universitat Politècnica de Catalunya, BarcelonaTech, CommSensLab - Upc, Spain ; Institute of Space Studies of Catalonia (IEEC), CTE-UPC, Spain
QUEROL, Jorge ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom ; Universitat Politècnica de Catalunya, BarcelonaTech, CommSensLab - Upc, Spain
Camps, Adriano; Universitat Politècnica de Catalunya, BarcelonaTech, CommSensLab - Upc, Spain ; Institute of Space Studies of Catalonia (IEEC), CTE-UPC, Spain ; Uae University, CoE, Al-Ain, United Arab Emirates
External co-authors :
yes
Language :
English
Title :
Classification and Characterization of RFI Events for Passive Earth Observation Bands
Publication date :
07 July 2024
Event name :
IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium
Event place :
Athens, Grc
Event date :
07-07-2024 => 12-07-2024
Audience :
International
Main work title :
IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, Proceedings
Publisher :
Institute of Electrical and Electronics Engineers Inc.
R. Oliva and E. Daganzo, "SMOS Radio Frequency Interference Scenario: Status and Actions Taken to Improve the RFI Environment in the 1400-1427 MHz Passive Band, " IEEE Transactions on Geoscience and Remote Sensing, vol. 50, no. 5, pp. 1427-1439, May 2012.
J. Querol, R. Onrubia, D. Pascual, H. Park, and A. Camps, "A Radio-Frequency Interference detector for GNSS navigation and GNSS-reflectometry applications, " in International Geoscience and Remote Sensing Symposium (IGARSS). July 2017, vol. 2017-July, IEEE.
A. Perez-Portero, J. Querol, and A. Camps, "Real-Time Direction Of Arrival Estimation And Characterization Of RFI Signals In GNSS Bands, " in IEEE International Geoscience and Remote Sensing Symposium, 2023.
S. Scholl, "RF signal classification with synthetic training data and its real-world performance, " 2022.
R. D. De Roo, S. Misra, and C. S. Ruf, "Sensitivity of the Kurtosis Statistic as a Detector of Pulsed Sinusoidal RFI, " IEEE Transactions on Geoscience and Remote Sensing, vol. 45, no. 7, pp. 1938-1946, July 2007.
J. M. Tarongi and A. Camps, "Radio frequency interference detection and mitigation algorithms based on spectrogram analysis, " Algorithms, vol. 4, no. 4, pp. 239-261, 2011.
D. Welch, "Spectrum analyzer measurements, " Tech. Rep., TekTronix, 1969.
R. Diez-Garcia, A. Camps, and H. Park, "RFI mitigation in microwave radiometry using the Karhunen-Loève transform, " IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-13, 2023.
P. N. Mohammed and J. R. Piepmeier, "Microwave radiometer RFI detection using deep learning, " IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 6398-6405, 2021.
T. Erpek, T. J. O'Shea, Y. E. Sagduyu, Y. Shi, and T. C. Clancy, "Deep learning for wireless communications, " 2020.
N. M. Foumani, L. Miller, C. W. Tan, G. I. Webb, G. Forestier, and M. Salehi, "Deep learning for time series classification and extrinsic regression: A current survey, " 2023.
S. Subray, S. Tschimben, and K. Gifford, "Towards enhancing spectrum sensing: Signal classification using autoencoders, " IEEE Access, vol. 9, pp. 82288-82299, 2021.
