A Local Search Maximum Likelihood Parameter Estimator of Chirp Signal

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 assi...

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Bibliographic Details
Published in:Applied sciences 2021-01, Vol.11 (2), p.673
Main Authors: Ben, Guangli, Zheng, Xifeng, Wang, Yongcheng, Zhang, Ning, Zhang, Xin
Format: Article
Language:English
Subjects:
Accuracy
chirp parameter estimation
Chirp signals
Deep learning
maximum likelihood
Methods
Neural networks
Noise reduction
Optimization
Parameter estimation
Searching
signal denoising
Signal processing
Signal to noise ratio
time-frequency analysis
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Summary: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.
ISSN:2076-3417
2076-3417
DOI:10.3390/app11020673