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A Local Search Maximum Likelihood Parameter Estimator of Chirp Signal

Ben, Guangli ; Zheng, Xifeng ; Wang, Yongcheng ; Zhang, Ning ; Zhang, Xin

Applied sciences, 2021-01, Vol.11 (2), p.673 [Periódico revisado por pares]

Basel: MDPI AG

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Título:
A Local Search Maximum Likelihood Parameter Estimator of Chirp Signal
Autor: Ben, Guangli ; Zheng, Xifeng ; Wang, Yongcheng ; Zhang, Ning ; Zhang, Xin
Assuntos: Accuracy ; Artificial neural networks ; chirp parameter estimation ; Chirp signals ; Deep learning ; maximum likelihood ; Maximum likelihood estimators ; Methods ; Neural networks ; Noise reduction ; Optimization ; Parameter estimation ; Searching ; signal denoising ; Signal processing ; Signal to noise ratio ; time-frequency analysis
É parte de: Applied sciences, 2021-01, Vol.11 (2), p.673
Descrição: 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.
Editor: Basel: MDPI AG
Idioma: Inglês

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A Local Search Maximum Likelihood Parameter Estimator of Chirp Signal

Ben, Guangli ; Zheng, Xifeng ; Wang, Yongcheng ; Zhang, Ning ; Zhang, Xin

Basel: MDPI AG

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