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 met...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.展开更多
Design and realization of random measurement scheme for compressed sensing （CS） are presented in this paper, and lower limits of the measurement number are achieved when the precise reconstruction is realized. Four ...Design and realization of random measurement scheme for compressed sensing （CS） are presented in this paper, and lower limits of the measurement number are achieved when the precise reconstruction is realized. Four kinds of random measurement matrices are designed according to the constraint conditions of random measurement. The performance is tested employing the algorithm of stagewise orthogonal matching pursuit （STOMP）. Results of the experiment show that lower limits of the measurement number are much better than the results described in Refs.[ 13-15]. When the ratios of measurement to sparsity are 3.8 and 4.0, the mean relative errors of the reconstructed signals are 8.57 × 10^-13 and 2.43 × 10^-14, respectively, which confh-rns that the random measurement scheme of this paper is very effective.展开更多
基金supported by the National Natural Science Foundation of China(No.71874081)Special Financial Grant from China Postdoctoral Science Foundation(No.2017T100366)Open Fund of Hebei Province Key laboratory of Research on data analysis method under dynamic electro-magnetic spectrum situation.
文摘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.
基金supported by the National Natural Science Foundation of China(Nos.61072111 and 60672156)the Project of Science and Technology Commission of Jilin Province(Nos.20100503 and 20110360)
文摘Design and realization of random measurement scheme for compressed sensing （CS） are presented in this paper, and lower limits of the measurement number are achieved when the precise reconstruction is realized. Four kinds of random measurement matrices are designed according to the constraint conditions of random measurement. The performance is tested employing the algorithm of stagewise orthogonal matching pursuit （STOMP）. Results of the experiment show that lower limits of the measurement number are much better than the results described in Refs.[ 13-15]. When the ratios of measurement to sparsity are 3.8 and 4.0, the mean relative errors of the reconstructed signals are 8.57 × 10^-13 and 2.43 × 10^-14, respectively, which confh-rns that the random measurement scheme of this paper is very effective.
