Signal reconstruction method based on generative adversarial network

Abstract

The invention belongs to the technical field of radio signal reconstruction, and discloses a signal reconstruction method based on a generative adversarial network. The method includes the following steps: under the framework of the generative adversarial network, constructing a generator for generating a signal and a discriminator for judging whether the signal is real data, and updating and optimizing parameters of the generator through the cross training of the generator and the discriminator. The scheme of the invention is suitable for the signal reconstruction in a complex electromagneticenvironment, has the characteristics of simple operating process, high similarity of generated data and the like, and effectively overcomes the shortcomings of low similarity of generated samples andinsufficient sample diversity existing in a current signal reconstruction method; and the scheme of the invention proposes a method of implementing the signal reconstruction by using the generative adversarial network, the cross-game training of the generative adversarial network is adopted, signal features are extracted through the mapping of a network layer, the cumbersome and inefficient process of performing parameter measurement, feature extraction and the like on the signal can be eliminated, and the problem of difficulties in signal analysis in the complex electromagnetic environmentscan be solved.

Description

technical field [0001] The invention belongs to the technical field of communication countermeasures, in particular to a signal reconstruction method based on a generative countermeasure network. That is, based on the technology of generative confrontation network, a neural network that automatically generates signals is built as a generator and a discriminator to judge the quality of generated data, which is suitable for reconstructing signals that receive noise interference in electromagnetic environments. Background technique [0002] At present, the existing technologies commonly used in the industry are as follows: [0003] In the technical field of radio signal reconstruction, traditional signal reconstruction adopts a technical system based on parameter measurement, feature extraction, and signal reconstruction. This signal reconstruction system is relatively easy to reconstruct radio signals in an electromagnetic environment with low information density and few com...

AI Technical Summary

Problems solved by technology

[0005] (1) Traditional communication signal reconstruction is based on the system of parameter measurement, feature extraction, and signal reconstruction. It is difficult to obtain important parameters and feature information from a wide variety of signals in an increasingly complex electromagnetic environment, making the reconstructed Signal accuracy is low;

[0006] (2) In the application of the existing generative adversarial network algorithm to generate signals, there are problems that it is difficult to accurately extract and learn the sequence characteristics of the signal, and the gradient of the neural network layer disappears seriously, which leads to the single diversity of signal data generated by reconstruction, and Disadvantages such as low fitting degree of original signal

[0008] In view of the above problems, the following difficulties are faced in the face of signal reconstruction: (1) how to accurately extract the key features and parameters of the signal in a complex electromagnetic environment; (2) according to the prior information of the signal parameters and features, how to Accurately reconstruct the generated signal, so that the reconstructed signal has a high similarity with the original signal; (3) How to make each generated data have a certain difference under the premise of satisfying the accuracy of the generated data

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