Sindhusha Jeeru

• Jeeru, Sindhusha; Omslandseter, Rebekka Olsson; Andersen, Per-Arne; Morrison, Aiden James; Sokolova, Nadezda & Granmo, Ole-Christoffer [Show all 7 contributors for this article] (2025). MixCTME: A Mixture of Convolutional Tsetlin Machine Experts Using Diverse Spectrogram Visualizations for Jamming Signal Classification. IEEE Internet of Things Journal. ISSN 2327-4662. 12(22), p. 47667–47678. doi: 10.1109/jiot.2025.3606366. Full text in Research Archive Show summary

  global navigation satellite systems (GNSSs) are vulnerable to jamming, which can degrade or even disable their services by interfering with signal reception. Such interference may interrupt GNSS positioning, navigation, timing, and communication functions. Therefore, robust jamming detection strategies are essential to ensure continuous and reliable services. Many existing jamming detection approaches use closed box machine learning (ML) models, which lack transparency and accuracy. In this article, we propose a novel mixture of convolutional Tsetlin machine experts (MixCTME) approach, using diverse spectrogram visualizations to enhance the accuracy and interpretability of jamming signal classification. To facilitate this, we collected raw in-phase and quadrature (IQ) data and named it the RealRFI dataset, which corresponds to ten different jamming classes. The data were collected through multiple radio-frequency interference (RFI) monitoring stations deployed at SINTEF companies across Europe and Scandinavia. Next, diverse visualizations were generated using short time Fourier transform (STFT), dominant frequency, fast Fourier transform (FFT), power spectral density, and standard deviation. These visualizations were binarized using the Otsu thresholding technique, and the binarized spectrograms were fed to the MixCTME model. Our method utilizes a combination of distinct convolutional Tsetlin machine (CTM) experts and employs a novel nonlinear technique, referred to as confidence-based gating, for weighting the experts’ inputs to make the final decision. We compared our method against state-of-the-art approaches via the self-collected and labeled dataset. Through experiments, our model achieved an accuracy of 90.70% on the collected data and 99.46% on a benchmark dataset, outperforming the state-of-the-art approaches. Additionally, we highlight the trustworthiness, scalability, and interpretability of the proposed approach, presenting a promising solution for accurate and interpretable jamming classification.

• Jeeru, Sindhusha; Lei, Jiao; Andersen, Per-Arne & Granmo, Ole-Christoffer (2025). Interpretable Rule-based Architecture for GNSS Jamming Signal Classification. IEEE Sensors Journal. ISSN 1530-437X. 25(10), p. 17942–17959. doi: 10.1109/JSEN.2025.3558966. Full text in Research Archive Show summary

  Jamming is a fatal threat to a global navigation satellite system (GNSS), and an efficient anti-jamming system relies on successful classification and identification of jamming types to respond effectively. The existing solutions suffer either from poor accuracy or lack of interpretability, and they are prone to learning simple statistical correlations rather than more fundamental and general relationships. In this study, we propose a novel approach to classify GNSS jamming signals as intentional or unintentional. The approach introduces a new standard deviation-based denoising method, which makes it possible to use the logical rule-based architecture of the convolutional Tsetlin machine (CTM) for interpretable jamming signal analysis. CTM is a recently developed algorithm that solves complex classification problems using conjunctive propositional formulas through a team of Tsetlin automata (TA). Unlike traditional opaque models based on deep learning, our approach goes beyond classification and provides a human-level interpretation of features. This interpretation capability allows a deeper comprehension of the characteristics and underlying patterns of the jamming signals, significantly easing the decision-making process. Furthermore, the CTM approach is also evaluated with different Booleanization techniques. Through experiments, we show that the proposed approach with CTM achieves an F1-score of 98.7%, on the collected dataset, which is superior to the state-of-the-art.

• Yeduri, Sreenivasa Reddy; Jeeru, Sindhusha; Pandey, Om Jee & Cenkeramaddi, Linga Reddy (2024). Energy-Efficient and Latency-Aware Data Routing in Small-World Internet of Drone Networks. IEEE Transactions on Network and Service Management. 21(6), p. 6555–6565. doi: 10.1109/TNSM.2024.3452414. Full text in Research Archive

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• Jeeru, Sindhusha (2024). Interpretable GNSS jamming classification with Tsetlin Machine. Full text in Research Archive
• Yeduri, Sreenivasa Reddy; Jeeru, Sindhusha & Cenkeramaddi, Linga Reddy (2023). Green machine learning protocols for machine-to-machine communication. In Saim, Ghafoor & Rehmani, Mubashir Husain (Ed.), Green Machine Learning Protocols for Future Communication Networks. CRC Press. ISSN 9781003230427. doi: 10.1201/9781003230427. Full text in Research Archive

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