An optimization synchrosqueezed fractional wavelet transform for TFF analysis and its applications

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing Pub Date : 2024-10-28 DOI:10.1016/j.dsp.2024.104819

Yong Guo , Lidong Yang

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Abstract

To enhance the resolution of synchrosqueezing transform (SST) in non-stationary signal representation, an optimization synchrosqueezed fractional wavelet transform (SSFRWT) is proposed, which possesses rigorous mathematical principle and high resolution. First, the definition, properties, and principles of SSFRWT are presented. On this basis, a time-fractional-frequency (TFF) analysis method is established utilizing SSFRWT. The experimental results demonstrate that SSFRWT is capable of establishing a high-resolution TFF representation for chirp-type signals, surpassing existing methods in terms of noise robustness and energy concentration. Lastly, leveraging the signal TFF representation, SSFRWT is successfully applied to the chirp signal parameter estimation and multi-component signal separation, yielding superior estimation results and reconstructed signal compared to SST. Notably, SSFRWT is also innovatively employed in the field of optical measurement, achieving high-precision measurement of the curvature radius of convex lens.

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用于 TFF 分析的优化同步queezed 小数小波变换及其应用

为了提高同步阙值变换（SST）在非平稳信号表示中的分辨率，提出了一种优化同步阙值分数小波变换（SSFRWT），它具有严谨的数学原理和高分辨率。首先，介绍了 SSFRWT 的定义、特性和原理。在此基础上，利用 SSFRWT 建立了时间-分数-频率（TFF）分析方法。实验结果表明，SSFRWT 能够为啁啾信号建立高分辨率的 TFF 表示，在噪声鲁棒性和能量集中方面超越了现有方法。最后，利用信号 TFF 表示，SSFRWT 成功应用于啁啾信号参数估计和多分量信号分离，与 SST 相比，获得了更优越的估计结果和重建信号。值得一提的是，SSFRWT 还创新性地应用于光学测量领域，实现了凸透镜曲率半径的高精度测量。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Digital Signal Processing 工程技术-工程：电子与电气

CiteScore

5.30

自引率

17.20%

发文量

435

审稿时长

66 days

期刊介绍： Digital Signal Processing: A Review Journal is one of the oldest and most established journals in the field of signal processing yet it aims to be the most innovative. The Journal invites top quality research articles at the frontiers of research in all aspects of signal processing. Our objective is to provide a platform for the publication of ground-breaking research in signal processing with both academic and industrial appeal. The journal has a special emphasis on statistical signal processing methodology such as Bayesian signal processing, and encourages articles on emerging applications of signal processing such as: • big data• machine learning• internet of things• information security• systems biology and computational biology,• financial time series analysis,• autonomous vehicles,• quantum computing,• neuromorphic engineering,• human-computer interaction and intelligent user interfaces,• environmental signal processing,• geophysical signal processing including seismic signal processing,• chemioinformatics and bioinformatics,• audio, visual and performance arts,• disaster management and prevention,• renewable energy,