Time-frequency Aliasing Separation Method of Radar Signal Based on Capsule Neural Network

Zhu, Xingsheng; Chang, Cheng; Yang, Limin; Deng, Zhijun; Cen, Xiaofeng

doi:10.1109/iccc51575.2020.9345131

Cited by 2 publications

(1 citation statement)

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“…In the data generation and mixing part, communication data from five modulation modes, including BPSK, 8PSK, QAM16, QAM64, PAM4 were generated through the software-defined radio platform GNUradio [ 47 ], the sampling rate is 1 MHz, and the code rate is 125 K symbol/s. Referring to the existing research results [ 48 , 49 , 50 , 51 , 52 , 53 ] in Table 1 , combined with the actual operating efficiency of the simulation platform, the selected signal-to-noise ratio range was 5–20 dB, and the step size was 2.5 dB. In the simulation, it was assumed that the aliased signals from different sources had the same frequency offset and timing deviation.…”

Section: Methodsmentioning

confidence: 99%

Single-Channel Blind Source Separation of Spatial Aliasing Signal Based on Stacked-LSTM

Zhao

Xiujuan

Wang

et al. 2021

Sensors

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Aiming at the problem of insufficient separation accuracy of aliased signals in space Internet satellite-ground communication scenarios, a stacked long short-term memory network (Stacked-LSTM) separation method based on deep learning is proposed. First, the coding feature representation of the mixed signal is extracted. Then, the long sequence input is divided into smaller blocks through the Stacked-LSTM network with the attention mechanism of the SE module, and the deep feature mask of the source signal is trained to obtain the Hadamard product of the mask of each source and the coding feature of the mixed signal, which is the encoding feature representation of the source signal. Finally, characteristics of the source signal is decoded by 1-D convolution to to obtain the original waveform. The negative scale-invariant source-to-noise ratio (SISNR) is used as the loss function of network training, that is, the evaluation index of single-channel blind source separation performance. The results show that in the single-channel separation of spatially aliased signals, the Stacked-LSTM method improves SISNR by 10.09∼38.17 dB compared with the two classic separation algorithms of ICA and NMF and the three deep learning separation methods of TasNet, Conv-TasNet and Wave-U-Net. The Stacked-LSTM method has better separation accuracy and noise robustness.

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