Time-Series LPI Signal Classification and Relevance Analysis Using BiLSTM-Attention with LRP 


Vol. 49,  No. 12, pp. 1695-1697, Dec.  2024
10.7840/kics.2024.49.12.1695


PDF Full-Text
  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.

  Statistics
Cumulative Counts from November, 2022
Multiple requests among the same browser session are counted as one view. If you mouse over a chart, the values of data points will be shown.


  Related Articles
  Cite this article

[IEEE Style]

K. Park and H. Nam, "Time-Series LPI Signal Classification and Relevance Analysis Using BiLSTM-Attention with LRP," The Journal of Korean Institute of Communications and Information Sciences, vol. 49, no. 12, pp. 1695-1697, 2024. DOI: 10.7840/kics.2024.49.12.1695.

[ACM Style]

Kiwan Park and Haewoon Nam. 2024. Time-Series LPI Signal Classification and Relevance Analysis Using BiLSTM-Attention with LRP. The Journal of Korean Institute of Communications and Information Sciences, 49, 12, (2024), 1695-1697. DOI: 10.7840/kics.2024.49.12.1695.

[KICS Style]

Kiwan Park and Haewoon Nam, "Time-Series LPI Signal Classification and Relevance Analysis Using BiLSTM-Attention with LRP," The Journal of Korean Institute of Communications and Information Sciences, vol. 49, no. 12, pp. 1695-1697, 12. 2024. (https://doi.org/10.7840/kics.2024.49.12.1695)
Vol. 49, No. 12 Index

Search
(IN TITLE, AUTHOR, ABSTRACT,KEYWORDS)

Advanced Search ▼

POPULAR KEYWORDS
(TOP 10 KEYWORDS)

Recent Publications
(LAST 3 YEARS)






pISSN : 1226 - 4717
eISSN : 2287 - 3880