HDCGD-CBAM:Satellite Interference Recognition Algorithm Based on Improved CLDNN and CBAM  

作　　者：Duan Ruifeng Chen Ziyu Meng Wei Wang Xu Yang Guoting Cheng Peng Li Yonghui 

机构地区：[1]School of Information Science and Technology,Beijing Forestry University,Beijing 100083,China [2]Engineering Research Center for Forestry-Oriented Intelligent Information Processing of National Forestry and Grassland Administration,Beijing 100083,China [3]Jining Economic and Social Development Research Center,Jining 272000,China [4]School of Electrical and Information Engineering,the University of Sydney,Sydney,NSW 2006,Australia [5]Department of Computer Science and Information Technology,La Trobe University,Melbourne,VIC 3086,Australia

出　　处：《China Communications》2024年第12期257-274,共18页中国通信（英文版）

基　　金：This work was supported by the Beijing Natural Science Foundation(L202003).

摘　　要：Satellite communication systems are facing serious electromagnetic interference,and interference signal recognition is a crucial foundation for targeted anti-interference.In this paper,we propose a novel interference recognition algorithm called HDCGD-CBAM,which adopts the time-frequency images(TFIs)of signals to effectively extract the temporal and spectral characteristics.In the proposed method,we improve the Convolutional Long Short-Term Memory Deep Neural Network(CLDNN)in two ways.First,the simpler Gate Recurrent Unit(GRU)is used instead of the Long Short-Term Memory(LSTM),reducing model parameters while maintaining the recognition accuracy.Second,we replace convolutional layers with hybrid dilated convolution(HDC)to expand the receptive field of feature maps,which captures the correlation of time-frequency data on a larger spatial scale.Additionally,Convolutional Block Attention Module(CBAM)is introduced before and after the HDC layers to strengthen the extraction of critical features and improve the recognition performance.The experiment results show that the HDCGD-CBAM model significantly outper-forms existing methods in terms of recognition accuracy and complexity.When Jamming-to-Signal Ratio(JSR)varies from-30dB to 10dB,it achieves an average accuracy of 78.7%and outperforms the CLDNN by 7.29%while reducing the Floating Point Operations(FLOPs)by 79.8%to 114.75M.Moreover,the proposed model has fewer parameters with 301k compared to several state-of-the-art methods.

关 键 词：attention mechanism CLDNN HDC interference recognition satellite communication 

分 类 号：TN927.2[电子电信—通信与信息系统]