@article{M801A7975,
title = "Time-Series LPI Signal Classification and Relevance Analysis Using BiLSTM-Attention with LRP",
journal = "The Journal of Korean Institute of Communications and Information Sciences",
year = "2024",
issn = "1226-4717",
doi = "10.7840/kics.2024.49.12.1695",
author = "Kiwan Park, Haewoon Nam",
keywords = "LPI Signal Classification, Layer wise Relevance Propagation, BiLSTM, Self Attention, Electronic Warfare (EW)",
abstract = "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."
}