Transactions of Nanjing University of Aeronautics & Astronautics
A Signal Recognition Algorithm Based on Compressive Sensing and Improved Residual Network at Airport Terminal Area
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Abstract:
It is particular important to identify the pattern of communication signal quickly and accurately at the airport terminal area with the increasing number of radio equipments. A signal modulation pattern recognition method based on compressive sensing and improved residual network is proposed in this work. Firstly， the compressive sensing method is introduced in the signal preprocessing process to discard the redundant components for sampled signals. And the compressed measurement signals are taken as the input of the network. Furthermore， based on a scaled exponential linear units activation function， the residual unit and the residual network are constructed in this work to solve the problem of long training time and indistinguishable sample similar characteristics. Finally， the global residual is introduced into the training network to guarantee the convergence of the network. Simulation results show that the proposed method has higher recognition efficiency and accuracy compared with the state-of-the-art deep learning methods.
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Project Supported:
This work was supported by the National Natural Science Foundation of China （No.71874081）， Special Financial Grant from China Postdoctoral Science Foundation（No.2017T100366） and Open Fund of Hebei Province Key laboratory of Research on data analysis method under dynamic electro-magnetic spectrum situation.
Clc Number:
U8
CHINA AERO-SCIENCES
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