Fuzheng Liu , Chenglong Ye , Chang Peng , Xiangyi Geng , Mingshun Jiang , Lei Zhang , Faye Zhang
{"title":"Residual-optimized general linear chirplet transform: A method for time–frequency feature extraction","authors":"Fuzheng Liu , Chenglong Ye , Chang Peng , Xiangyi Geng , Mingshun Jiang , Lei Zhang , Faye Zhang","doi":"10.1016/j.sigpro.2025.110006","DOIUrl":null,"url":null,"abstract":"<div><div>Time–frequency analysis (TFA) is an essential sub-area in signal processing, and many of its methods are widely used in various fields. Current methods cannot address the multi-component signals well when facing strong noise, which makes it difficult to describe the accurate time–frequency trajectory. This paper proposes a TFA method named residual optimized general linear chirplet transform (ROGLCT) to achieve better time–frequency energy concentration and robustness. Firstly, the beetle antennae search (BAS) algorithm with Rényi entropy was utilized as the fitness function to optimize GLCT parameters. Then, the maximum energy modes of the optimized GLCT time–frequency distribution are iteratively separated. Finally, the separated modes are squeezed to rebuild the spectrum. Numerical simulation and actual signals (echolocation and vibration signals) verify the proposed method’s effectiveness. Compared with other advanced methods, ROGLCT can overcome noise interference and depict clear, energy-concentrated time–frequency representation (TFR) in complex environments.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"234 ","pages":"Article 110006"},"PeriodicalIF":3.4000,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Signal Processing","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0165168425001203","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
Time–frequency analysis (TFA) is an essential sub-area in signal processing, and many of its methods are widely used in various fields. Current methods cannot address the multi-component signals well when facing strong noise, which makes it difficult to describe the accurate time–frequency trajectory. This paper proposes a TFA method named residual optimized general linear chirplet transform (ROGLCT) to achieve better time–frequency energy concentration and robustness. Firstly, the beetle antennae search (BAS) algorithm with Rényi entropy was utilized as the fitness function to optimize GLCT parameters. Then, the maximum energy modes of the optimized GLCT time–frequency distribution are iteratively separated. Finally, the separated modes are squeezed to rebuild the spectrum. Numerical simulation and actual signals (echolocation and vibration signals) verify the proposed method’s effectiveness. Compared with other advanced methods, ROGLCT can overcome noise interference and depict clear, energy-concentrated time–frequency representation (TFR) in complex environments.
期刊介绍:
Signal Processing incorporates all aspects of the theory and practice of signal processing. It features original research work, tutorial and review articles, and accounts of practical developments. It is intended for a rapid dissemination of knowledge and experience to engineers and scientists working in the research, development or practical application of signal processing.
Subject areas covered by the journal include: Signal Theory; Stochastic Processes; Detection and Estimation; Spectral Analysis; Filtering; Signal Processing Systems; Software Developments; Image Processing; Pattern Recognition; Optical Signal Processing; Digital Signal Processing; Multi-dimensional Signal Processing; Communication Signal Processing; Biomedical Signal Processing; Geophysical and Astrophysical Signal Processing; Earth Resources Signal Processing; Acoustic and Vibration Signal Processing; Data Processing; Remote Sensing; Signal Processing Technology; Radar Signal Processing; Sonar Signal Processing; Industrial Applications; New Applications.