Reference : Radio Frequency Interference Detection Using Nonnegative Matrix Factorization
Scientific journals : Article
Engineering, computing & technology : Electrical & electronics engineering
Security, Reliability and Trust
http://hdl.handle.net/10993/49121
Radio Frequency Interference Detection Using Nonnegative Matrix Factorization
English
Silva, Felipe B. mailto []
Cetin, Ediz mailto []
Alves Martins, Wallace mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom >]
2021
IEEE Transactions on Aerospace and Electronic Systems
Institute of Electrical and Electronics Engineers (IEEE)
Yes (verified by ORBilu)
International
0018-9251
1557-9603
Piscataway
NJ
[en] Global navigation satellite systems ; interference detection ; time-frequency analysis ; jamming detection ; nonnegative matrix factorization
[en] This work proposes a new pre-correlation interference detection technique based on nonnegative matrix factorization (NMF) for global navigation satellite system (GNSS) signals. The proposed technique uses NMF to extract the time and frequency properties of the received signal from its spectrogram. The estimated spectral shape is then compared with the spectrogram’s time slices by means of a similarity function to detect the presence of radio frequency interference (RFI). In the presence of RFI, the NMF estimated spectral shape tends to be well-defined, resulting in high similarity levels. In contrast, in the absence of RFI, the received signal is solely comprised of noise and GNSS signals resulting in a noise like spectral shape estimate, leading to considerably reduced similarity levels. The proposal exploits this different similarity levels to detect the presence of interference. Simulation results indicate that the proposed technique yields increased detection capability with low false alarm rate even in low jammer-to-noise ratio environments for both narrow and wideband interference sources without requiring fine-tuning of parameters for specific RFI types. In addition, the proposal has reduced computational complexity, when compared with an existing statistical-based detector.
http://hdl.handle.net/10993/49121
10.1109/TAES.2021.3111730

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