Chirp Signal Denoising Based on Convolution Neural Network

doi:10.1007/s00034-021-01727-4

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	Chirp Signal Denoising Based on Convolution Neural Network
	G. Ben; X. Zheng; Y. Wang; X. Zhang and N. Zhang
	2021
发表期刊	Circuits, Systems, and Signal Processing
ISSN	0278081X
卷号	40 期号:11 页码:5468-5482
摘要	Many classic chirp signal processing algorithms may show significant performance degradation when the signal-to-noise ratio (SNR) is low. To address this problem, this paper proposes a pre-filtering method in time-domain based on deep learning. Different from traditional signal filtering methods, the proposed denoising convolutional neural network (DCNN) is trained to recover the pure signal from the noisy signal as much as possible. Following denoising, we use two classic chirp signal parameter estimation algorithms to estimate the parameters of the DCNN output. The simulation results show that, compared with no DCNN processing, the parameter estimation accuracy is significantly improved. This is mainly due to the powerful pure signal extraction ability of DCNN, which can significantly improve the SNR and the accuracy of signal parameter estimation. 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
DOI	10.1007/s00034-021-01727-4
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收录类别	SCI ; EI
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文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/65093
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	G. Ben,X. Zheng,Y. Wang,et al. Chirp Signal Denoising Based on Convolution Neural Network[J]. Circuits, Systems, and Signal Processing,2021,40(11):5468-5482.
APA	G. Ben,X. Zheng,Y. Wang,&X. Zhang and N. Zhang.(2021).Chirp Signal Denoising Based on Convolution Neural Network.Circuits, Systems, and Signal Processing,40(11),5468-5482.
MLA	G. Ben,et al."Chirp Signal Denoising Based on Convolution Neural Network".Circuits, Systems, and Signal Processing 40.11(2021):5468-5482.

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