{"title":"Optimal frequency sweep synthesis for the identification of low damped systems via a narrow-band method","authors":"D. Pech, G. Prokop","doi":"10.1016/j.sigpro.2025.110283","DOIUrl":null,"url":null,"abstract":"<div><div>System identification via sweep excitations suffers from transients in the case of high frequency rates and low system damping. This contribution presents a novel method for a time domain generation of sweep signals to accurately estimate the frequency response function of a linear system within a desired sweep time. The approach is based on a characteristic value for determining the harmony of a signal, which was previously presented by the authors. It has been empirically found that this characteristic value is directly related to the squared derivative of the period duration of a sweep signal. Therefore, it can be used to shape a desired frequency characteristic in a way that suppresses transient effects of the system response compared to basic sweep approaches. The method is optimized to identify a single degree of freedom oscillator via particle swarm optimization. It is shown that the identification via an envelope of the system response can be enhanced by approximately 70 <span><math><mo>%</mo></math></span> compared to basic sweep signals for a weak damped oscillator. Therefore, the approach mitigates the trade-off between time requirements and accuracy of system identification via sweep excitations, if a rough estimate of the resonant frequency and the damping ratio is available.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"239 ","pages":"Article 110283"},"PeriodicalIF":3.6000,"publicationDate":"2025-09-09","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/S0165168425003974","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
System identification via sweep excitations suffers from transients in the case of high frequency rates and low system damping. This contribution presents a novel method for a time domain generation of sweep signals to accurately estimate the frequency response function of a linear system within a desired sweep time. The approach is based on a characteristic value for determining the harmony of a signal, which was previously presented by the authors. It has been empirically found that this characteristic value is directly related to the squared derivative of the period duration of a sweep signal. Therefore, it can be used to shape a desired frequency characteristic in a way that suppresses transient effects of the system response compared to basic sweep approaches. The method is optimized to identify a single degree of freedom oscillator via particle swarm optimization. It is shown that the identification via an envelope of the system response can be enhanced by approximately 70 compared to basic sweep signals for a weak damped oscillator. Therefore, the approach mitigates the trade-off between time requirements and accuracy of system identification via sweep excitations, if a rough estimate of the resonant frequency and the damping ratio is available.
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.