이미지 변환을 통한 RF 신호 기반의 드론 및 성능 평가

Vol. 34, No. 6, pp. 1283-1295, 12월. 2024
10.13089/JKIISC.2024.34.6.1283, Full Text:
Keywords: Image Transformation, RF signals, Convolutional Neural Network, Drone classification
Abstract

The rapid advancement of drone technology has led to increasing security threats, requiring effective countermeasures. This study presents a CNN-based model for drone classification using RF signals, processed with wavelet transform, STFT, and Mel-spectrogram. By optimizing parallel image conversion, the model achieves faster computation and supports real-time detection. The proposed approach enhances both classification accuracy and real-time performance compared to conventional methods.

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Cite this article
[IEEE Style]
조준영, 최두호, 한미란, 오진섭, 이한빈, 조리노, "Drone Classification and Performance Evaluation Based on RF Signal through Image Transformation," Journal of The Korea Institute of Information Security and Cryptology, vol. 34, no. 6, pp. 1283-1295, 2024. DOI: 10.13089/JKIISC.2024.34.6.1283.

[ACM Style]
조준영, 최두호, 한미란, 오진섭, 이한빈, and 조리노. 2024. Drone Classification and Performance Evaluation Based on RF Signal through Image Transformation. Journal of The Korea Institute of Information Security and Cryptology, 34, 6, (2024), 1283-1295. DOI: 10.13089/JKIISC.2024.34.6.1283.