A deep learning-based interference effect evaluation method and system

Abstract

The present application relates to a kind of interference effect evaluation method and system based on deep learning, its method includes the following steps: constructing neural network model, and using model training data to train neural network model, obtain interference evaluation model;Wherein, model training data includes normal communication data, interference communication data and composite communication data;Normal communication data is the communication waveform data when not applying interference;Interference communication data is the communication waveform data after applying high-power intensive interference and interference succeeds;Composite communication data is the communication waveform data when the power of interference signal is less than the power of useful signal;Communication signal under communication interference is collected, and perception waveform data is obtained;Perception waveform data is input into interference evaluation model for evaluation, and interference effect evaluation result is obtained;The present application is convenient in electronic countermeasure, and interference party can correctly judge whether its interference is effective.

Images