Band Correlation-Based Multichannel Multiscale Convolution Network for Intelligent Interference Recognition

In recent years, with the development and extensive application of wireless communication technology, the communication system should have stronger anti-Jamming ability. Therefore, interference recognition is particularly important as a prerequisite for anti-interference. However, the existing traditional and intelligent interference recognition algorithms have problems such as complicated feature extraction and low recognition accuracy under low interference-to-noise ratio. In order to solve the above problems, this paper introduces parallel multi-channel multi-scale convolution to improve the speed and accuracy of network recognition. In addition, combined with frequency band correlation and long-short-term memory network (LSTM), an innovative wireless communication interference identification model based on frequency band correlation is proposed, which uses LSTM to detect the frequency band correlation of interference signals and improve the accuracy of interference identification under low Jamming noise ratio (JNR). Experiments prove that the model proposed in this article has faster recognition speed and better generalization. The introduction of frequency band correlation increases the recognition accuracy to more than 99% with low JNR. Therefore, the model proposed in this paper is an effective and available model in complex electromagnetic environments.