Electromagnetic Signal Classification Based on Deep Sparse Capsule Networks

IEEE Access

Fengkui Gong¹  Mingqian Liu²  Zhutian Yang²  Guiyue Liao³  Hao Song³ 

[1] School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, China;State Key Laboratory of Integrated Service Network, Xidian University, Xi&x2019;an, China;

关键词: Signal classification;    capsule networks;    sparse filtering;    cross ambiguity function;    higher order amplitude spectrum;   

DOI  :  10.1109/ACCESS.2019.2924798

来源: DOAJ

【 摘 要 】

In complex electromagnetic environments, electromagnetic signal classification rates are low as long time have to be the cost to extract features. To cope with the issue, in this paper, an electromagnetic signal classification method is proposed based on deep sparse capsule networks. In the proposed method, received signals are frequency reduced and sampled processing first. Subsequently, a cross ambiguity function based on linear canonical transformation, a cross ambiguity function based on linear canonical domain, and higher-order spectrum are estimated, respectively. The maximum value of each section of the cross ambiguity function is combined with the maximum value of equally spaced cross sections of higher order amplitude spectrum to obtain the two-dimensional feature information. Finally, electromagnetic signals are classified by the deep sparse capsule networks. The simulation results show that the proposed method not only has good classification performance but also can automatically get a hierarchical feature representation by learning. Moreover, the corresponding time cost can be effectively reduced.

【 授权许可】

Unknown