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BiLSTM-Attention과 LRP를 활용한 시계열 LPI 신호분류 및 중요도 분석
Time-Series LPI Signal Classification and Relevance Analysis Using BiLSTM-Attention with LRP
- Park, Kiwan;
- Nam, Haewoon
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0초록
This study applies a BiLSTM-Attention model and Layer-wise Relevance Propagation (LRP) to classify and analyze the importance of low probability of intercept (LPI) signals. The goal is to interpret the predictions of a time-series trained model using LRP and effectively identify meaningful input features. The analysis shows that the model maintains high consistency in its prediction rationale even in the frequency domain, transformed through Fast Fourier Transform (FFT). Experiments across various Signal-to-Noise Ratio (SNR) conditions confirm that the model delivers reliable classification performance while ensuring stable detection of key features through LRP-based interpretation. © 2024, Author. All rights reserved.
키워드
BiLSTM; Electronic Warfare (EW); Layer wise Relevance Propagation; LPI Signal Classification; Self Attention; LPI Signal Classification; Layer wise Relevance Propagation; BiLSTM; Self Attention; Electronic Warfare (EW
- 제목
- BiLSTM-Attention과 LRP를 활용한 시계열 LPI 신호분류 및 중요도 분석
- 제목 (타언어)
- Time-Series LPI Signal Classification and Relevance Analysis Using BiLSTM-Attention with LRP
- 저자
- Park, Kiwan; Nam, Haewoon
- 발행일
- 2024-12
- 유형
- Article
- 저널명
- 한국통신학회논문지
- 권
- 49
- 호
- 12
- 페이지
- 1695 ~ 1697