Working mode recognition of the typical radiation source based on one-dimensional time-frequency features

Haitao Li; Yingke Lei; Caiyi Lou

doi:10.1117/12.2674305

28 April 2023 Working mode recognition of the typical radiation source based on one-dimensional time-frequency features

Haitao Li, Yingke Lei, Caiyi Lou

Proceedings Volume 12626, International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022); 1262612 (2023) https://doi.org/10.1117/12.2674305
Event: International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022), 2022, Zhuhai, China

Abstract

By identifying the working mode of the communication radiation source, its behavioral intent can be comprehended. Tactical data link is a kind of classic communication radiation source with a variety of working modes. Aiming at the typical data link TADIL A, a method for extracting one-dimensional time-frequency features of signals using short-time Fourier transform (STFT) is proposed. Then, one-dimensional Convolutional Neural Network (CNN) DenseNet-1D is used for training and testing to complete the task of identifying different working modes. The experimental results illustrate that the working mode recognition based on the physical layer signal is feasible.

Citation Download Citation

Haitao Li, Yingke Lei, and Caiyi Lou "Working mode recognition of the typical radiation source based on one-dimensional time-frequency features", Proc. SPIE 12626, International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022), 1262612 (28 April 2023); https://doi.org/10.1117/12.2674305

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