Abstract
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.
Highlights
IntroductionChirp signals have a broad range of applications, such as in radar, sonar, communication, and medical imaging [1,2]
Inspired by the above ideas, in this paper, we propose a local search maximum likelihood parameter estimator based on the deep learning denoising approach for chirp parameter estimation in low Signal-to-Noise Ratio (SNR) conditions
The rotation angle of Radon–Wigner Transform (RWT) is from 85 to 91 degrees with an interval of 0.01, the fractional power of FrFT is from 0.96 to 1.08 with an interval of 0.0001, the performance metric is the natural logarithm of Root Mean Squared Error, as described in Equations (10) and (11)
Summary
Chirp signals have a broad range of applications, such as in radar, sonar, communication, and medical imaging [1,2]. Chirp usage and estimation of their parameters, i.e., the initial frequency f0 and chirp rate k, is a significant part in the digital signal processing area [3,4]. The main trends in research on chirp signal parameter estimation are improving estimation accuracy, increasing computational efficiency, and enhancing adaptation to low SNRs [5]. TF analysis is an efficient tool to analyze the behavior of nonstationary signals [3]. TF transform, e.g., Short-Time Fourier Transform (STFT) [6] and Wigner-Ville Distribution (WVD) [7,8], can convert signals from one-dimensional time domain to two-dimensional
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