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   Radio Frequency Interference Detection Using Nonnegative Matrix Factorization; ; Alves Martins, Wallace  in IEEE Transactions on Aerospace and Electronic Systems (2021) 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 ... [more ▼] 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. [less ▲] Detailed reference viewed: 68 (1 UL)DME Interference Mitigation for GNSS Receivers via Nonnegative Matrix Factorization; ; Alves Martins, Wallace in URSI GASS 2021, Rome 28 August - 4 September 2021 (2021) In this work a nonnegative matrix factorization based approach is proposed to mitigate the impact of interference due to distance measurement equipment (DME) signals in global navigation satellite system ... [more ▼] In this work a nonnegative matrix factorization based approach is proposed to mitigate the impact of interference due to distance measurement equipment (DME) signals in global navigation satellite system (GNSS) receivers. The proposed approach operates by separating the DME and GNSS signals, and the results show that it outperforms the traditional pulse-blanking based techniques in terms of acquisition and carrier-to-noise ratio metrics without discarding any of the received signal samples. [less ▲] Detailed reference viewed: 63 (1 UL)ADS-B Signal Detection via Time-Frequency Analysis for Radio Astronomy Applications; ; Alves Martins, Wallace in IEEE International Symposium on Circuits and Systems (ISCAS), Daegu 22-28 May 2021 (2021) This paper proposes a time-frequency (TF) domain technique for detecting the presence of automatic dependent surveillance-broadcast (ADS-B) interference signals in radio astronomy applications. The ... [more ▼] This paper proposes a time-frequency (TF) domain technique for detecting the presence of automatic dependent surveillance-broadcast (ADS-B) interference signals in radio astronomy applications. The proposed technique uses a priori knowledge about the ADS-B signal’s frequency information and compares it with the received signal’s spectrogram time slices via the cosine similarity function. In the presence of ADS-B signals, the similarity levels are higher, whereas in their absence the levels are lower. Hence, the proposed approach exploits this to detect the presence of such signals. Simulation results using signals from the Parkes radio telescope show the efficacy of the proposed method in detecting the presence of ADS-B signals when compared with other classic detectors. [less ▲] Detailed reference viewed: 48 (1 UL)A fault detector/classifier for closed-ring power generators using machine learning; ; et al in Reliability Engineering and System Safety (2021) Condition-based monitoring of power-generation systems is naturally becoming a standard approach in industry due to its inherent capability of fast fault detection, thus improving system efficiency and ... [more ▼] Condition-based monitoring of power-generation systems is naturally becoming a standard approach in industry due to its inherent capability of fast fault detection, thus improving system efficiency and reducing operational costs. Most such systems employ expertise-reliant rule-based methods. This work proposes a different framework, in which machine-learning algorithms are used for detecting and classifying several fault types in a power-generation system of dynamically positioned vessels. First, principal component analysis is used to extract relevant information from labeled data. A random-forest algorithm then learns hidden patterns from faulty behavior in order to infer fault detection from unlabeled data. Results on fault detection and classification for the proposed approach show significant improvement on accuracy and speed when compared to results from rule-based methods over a comprehensive database. [less ▲] Detailed reference viewed: 51 (0 UL) |
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