Abstract
This paper addresses the problem of estimating the parameters of constant-amplitude chirp signals that have single or multiple components and are embedded in noise. Chirp signals are widely employed in applications such as radar and telecommunications, and it is a key task in countermeasure techniques to estimate their parameters without prior information. Hence, a parameter estimation processor based on a complex-valued deep neural network (CV DNN) is proposed to perform this task efficiently. The CV DNN, which is designed for regression, consists of a function fitter and a predictor. The function fitter acts like a eigenfunction mapping: it maps the one-dimensional input into a two-dimensional feature map suitable for subsequent network learning. As a special feature extraction tool, the predictor extracts local features from the feature map and estimates parameters. Simulation results indicate that the CV DNN outperforms conventional processors. Moreover, it is more accurate than the Wigner-Hough transform while being several orders of magnitude faster, which will enable real-time signal processing with fewer computational resources. Furthermore, we demonstrate that the CV DNN shows strong robustness to changes in modulation parameters and the number of components of a chirp signal. This study shows the advantages of deep learning systems for signal parameter estimation.
Highlights
Linear frequency-modulated signals, which are called chirp signals, are widely employed in various applications such as radar [1], sonar [2], ultrasonics [3], and telecommunications [4]
The results indicate that both complex-valued deep neural network (CV deep neural network (DNN)) and Wigner–Hough transform (WHT) are insensitive to changes in Nc
Because of the strong advantages of neural networks in function fitting and feature extraction, we replace each part in the conventional processor with a neural network to achieve comparable or better performance
Summary
Linear frequency-modulated signals, which are called chirp signals, are widely employed in various applications such as radar [1], sonar [2], ultrasonics [3], and telecommunications [4]. A. TIME FREQUENCY ANALYSIS PROCESSORS Time-frequency-based methods have been reported to be effective for detecting and estimating chirp signals. TIME FREQUENCY ANALYSIS PROCESSORS Time-frequency-based methods have been reported to be effective for detecting and estimating chirp signals These techniques have attracted considerable attention and proved. H. Su et al.: Parameter Estimation Processor for Chirp Signals Based on a CV DNN themselves to be effective [11]–[20]. We propose a parameter estimation processor based on a DNN for chirp signals. A convolutional neural network (CNN), which is a special feature extraction tool for the feature map generated by the FCN, is used as a predictor to estimate parameters f0 and μ.
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