基于GAN的直扩信号生成算法

doi:10.12305/j.issn.1001-506X.2023.05.32

Abstract

Abstract:

It is of great practical significance to apply deep learning model to electronic jamming technology to generate jamming signals. The generative adversarial network (GAN) is applied to signal generation, and the electromagnetic spread spectrum signal is deeply learned by using the model, and the coherent interference signal is generated by learning the distribution of spectrum data of electromagnetic spread spectrum signal. In the experiment, the generator and discriminator of GAN are trained with each other and optimized by adaptive moment estimation (Adam). Finally, a good model can be trained and the required signals can be generated. Experimental results show that the generated data distribution based on the GAN signal generation algorithm basically has the characteristics of the real data distribution, and the generated data can accurately learn the different characteristics of the electromagnetic spectrum data with different signal to noise ratio (SNR) after deep learning of the same SNR data.

Key words: spread spectrum signal, spectrum data, generative adversarial network (GAN), electronic jamming

CLC Number:

TP391.41

Li CHEN, Zihan FANG, Liquan MEI. DSS signal generation algorithm based on GAN[J]. Systems Engineering and Electronics, 2023, 45(5): 1544-1552.

Figures/Tables 10

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DSS signal generation algorithm based on GAN

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