A Local Search Maximum Likelihood Parameter Estimator of Chirp Signal

doi:10.3390/app11020673

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	A Local Search Maximum Likelihood Parameter Estimator of Chirp Signal
	G. L. Ben; X. F. Zheng; Y. C. Wang; N. Zhang and X. Zhang
刊名	Applied Sciences-Basel
	2021
卷号	11 期号:2 页码:11
DOI	10.3390/app11020673
英文摘要	A local search Maximum Likelihood (ML) parameter estimator for mono-component chirp signal in low Signal-to-Noise Ratio (SNR) conditions is proposed in this paper. The approach combines a deep learning denoising method with a two-step parameter estimator. The denoiser utilizes residual learning assisted Denoising Convolutional Neural Network (DnCNN) to recover the structured signal component, which is used to denoise the original observations. Following the denoising step, we employ a coarse parameter estimator, which is based on the Time-Frequency (TF) distribution, to the denoised signal for approximate estimation of parameters. Then around the coarse results, we do a local search by using the ML technique to achieve fine estimation. Numerical results show that the proposed approach outperforms several methods in terms of parameter estimation accuracy and efficiency.
URL标识	查看原文
内容类型	期刊论文
源URL	[http://ir.ciomp.ac.cn/handle/181722/65391]
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. L. Ben,X. F. Zheng,Y. C. Wang,et al. A Local Search Maximum Likelihood Parameter Estimator of Chirp Signal[J]. Applied Sciences-Basel,2021,11(2):11.
APA	G. L. Ben,X. F. Zheng,Y. C. Wang,&N. Zhang and X. Zhang.(2021).A Local Search Maximum Likelihood Parameter Estimator of Chirp Signal.Applied Sciences-Basel,11(2),11.
MLA	G. L. Ben,et al."A Local Search Maximum Likelihood Parameter Estimator of Chirp Signal".Applied Sciences-Basel 11.2(2021):11.