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
刊名	Circuits, Systems, and Signal Processing
	2021
卷号	40 期号:11 页码:5468-5482
ISSN号	0278081X
DOI	10.1007/s00034-021-01727-4
英文摘要	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.
URL标识	查看原文
内容类型	期刊论文
源URL	[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.