{"title":"Estimating steady-state response of a resonant transducer in a reverberant underwater environment","authors":"J. D. George, V. Jain, P. Ainsleigh","doi":"10.1109/ICASSP.1988.197216","DOIUrl":null,"url":null,"abstract":"The authors examine the estimation of steady-state amplitude and phase using short, noisy records of the transient response of systems excited by a stepped sinusoid close to a resonance. Linear prediction estimation strategies are tested, and near maximum-likelihood (ML) performance is obtained by combining FIR (finite-impulse response) cancellation of the excitation poles with the truncated singular-value decomposition approach of R. Kumaresan and D.W. Tufts (1982). For the model tested, this excitation constrained estimation strategy departs from ML performance at a threshold signal-to-noise ratio that depends on the separation between the excitation and resonant frequencies.<<ETX>>","PeriodicalId":448544,"journal":{"name":"ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing","volume":"44 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1988-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICASSP.1988.197216","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
The authors examine the estimation of steady-state amplitude and phase using short, noisy records of the transient response of systems excited by a stepped sinusoid close to a resonance. Linear prediction estimation strategies are tested, and near maximum-likelihood (ML) performance is obtained by combining FIR (finite-impulse response) cancellation of the excitation poles with the truncated singular-value decomposition approach of R. Kumaresan and D.W. Tufts (1982). For the model tested, this excitation constrained estimation strategy departs from ML performance at a threshold signal-to-noise ratio that depends on the separation between the excitation and resonant frequencies.<>