Identification of Jamming Factors in Electronic Information System Based on Deep Learning

Abstract

Jamming identification is the precondition of taking targeted anti-jamming measures, and it is very important to improve the adaptability of electronic information system to electromagnetic environment. The traditional recognition method of jamming is based on the feature extraction based on expert knowledge, but due to the jamming pattern diversity and different parameter, in practice it is difficult to determine the appropriate feature set. Therefore, this paper introduces a deep learning approach, which automatically extracts features from the original data to identify the jamming factors of electronic information system. In order to demonstrate the effectiveness and practicability of this approach, the noise jamming factor identification of the superheterodyne receiver is introduced.