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논문 기본 정보

자료유형
학술저널
저자정보
Mi-Hyun Jin (GNSS R&D Center Danam Systems Anyang 13930 Korea) Ddeo-Ol-Ra Koo (GNSS R&D Center Danam Systems Anyang 13930 Korea) Kang-Suk Kim (GNSS R&D Center Danam Systems Anyang 13930 Korea)
저널정보
사단법인 항법시스템학회 Journal of Positioning, Navigation, and Timing Journal of Positioning, Navigation, and Timing 제11권 제3호
발행연도
2022.9
수록면
163 - 172 (10page)
DOI
10.11003/JPNT.2022.11.3.163

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Typical anti-jamming technologies based on array antennas, Space Time Adaptive Process (STAP) & Space Frequency Adaptive Process (SFAP), are very effective algorithms to perform nulling and beamforming. However, it does not perform equally well for all types of jamming signals. If the anti-jamming algorithm is not optimized for each signal type, antijamming performance deteriorates and the operation stability of the system become worse by unnecessary computation. Therefore, jamming classification technique is required to obtain optimal anti-jamming performance. Machine learning, which has recently been in the spotlight, can be considered to classify jamming signal. In general, performing supervised learning for classification requires a huge amount of data and new learning for unfamiliar signal. In the case of jamming signal classification, it is difficult to obtain large amount of data because outdoor jamming signal reception environment is difficult to configure and the signal type of attacker is unknown. Therefore, this paper proposes few-shot jamming signal classification technique using meta-learning and transfer-learning to train the model using a small amount of data. A training dataset is constructed by anti-jamming algorithm input data within the GNSS receiver when jamming signals are applied. For metalearning, Model-Agnostic Meta-Learning (MAML) algorithm with a general Convolution Neural Networks (CNN) model is used, and the same CNN model is used for transfer-learning. They are trained through episodic training using training datasets on developed our Python-based simulator. The results show both algorithms can be trained with less data and immediately respond to new signal types. Also, the performances of two algorithms are compared to determine which algorithm is more suitable for classifying jamming signals.

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