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Article: A New Regularized Adaptive Windowed Lomb Periodogram for Time-Frequency Analysis of Nonstationary Signals With Impulsive Components

TitleA New Regularized Adaptive Windowed Lomb Periodogram for Time-Frequency Analysis of Nonstationary Signals With Impulsive Components
Authors
KeywordsAdaptive window selection
Lomb periodogram
M-estimation
nonuniformly sampled data
power quality analysis
regularization
time-frequency analysis (TFA)
weighted least squares (WLS)
Issue Date2012
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=19
Citation
IEEE Transactions on Instrumentation and Measurement, 2012, v. 61 n. 8, p. 2283-2304 How to Cite?
AbstractThis paper proposes a new class of windowed Lomb periodogram (WLP) for time-frequency analysis of nonstationary signals, which may contain impulsive components and may be nonuniformly sampled. The proposed methods significantly extend the conventional Lomb periodogram in two aspects: 1) The nonstationarity problem is addressed by employing the weighted least squares (WLS) to estimate locally the time-varying periodogram and an intersection of confidence interval technique to adaptively select the window sizes of WLS in the time-frequency domain. This yields an adaptive WLP (AWLP) having a better tradeoff between time resolution and frequency resolution. 2) A more general regularized maximum-likelihood-type (M-) estimator is used instead of the LS estimator in estimating the AWLP. This yields a novel M-estimation-based regularized AWLP method which is capable of reducing estimation variance, accentuating predominant time-frequency components, restraining adverse influence of impulsive components, and separating impulsive components. Simulation results were conducted to illustrate the advantages of the proposed method over the conventional Lomb periodogram in adaptive time-frequency resolution, sparse representation for sinusoids, robustness to impulsive components, and applicability to nonuniformly sampled data. Moreover, as the computation of the proposed method at each time sample and frequency is independent of others, parallel computing can be conveniently employed without much difficulty to significantly reduce the computational time of our proposed method for real-time applications. The proposed method is expected to find a wide range of applications in instrumentation and measurement and related areas. Its potential applications to power quality analysis and speech signal analysis are also discussed and demonstrated.
Persistent Identifierhttp://hdl.handle.net/10722/155737
ISSN
2020 Impact Factor: 4.016
2015 SCImago Journal Rankings: 1.022
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorZhang, Zen_US
dc.contributor.authorChan, SCen_US
dc.contributor.authorWang, Cen_US
dc.date.accessioned2012-08-08T08:35:06Z-
dc.date.available2012-08-08T08:35:06Z-
dc.date.issued2012en_US
dc.identifier.citationIEEE Transactions on Instrumentation and Measurement, 2012, v. 61 n. 8, p. 2283-2304en_US
dc.identifier.issn0018-9456en_US
dc.identifier.urihttp://hdl.handle.net/10722/155737-
dc.description.abstractThis paper proposes a new class of windowed Lomb periodogram (WLP) for time-frequency analysis of nonstationary signals, which may contain impulsive components and may be nonuniformly sampled. The proposed methods significantly extend the conventional Lomb periodogram in two aspects: 1) The nonstationarity problem is addressed by employing the weighted least squares (WLS) to estimate locally the time-varying periodogram and an intersection of confidence interval technique to adaptively select the window sizes of WLS in the time-frequency domain. This yields an adaptive WLP (AWLP) having a better tradeoff between time resolution and frequency resolution. 2) A more general regularized maximum-likelihood-type (M-) estimator is used instead of the LS estimator in estimating the AWLP. This yields a novel M-estimation-based regularized AWLP method which is capable of reducing estimation variance, accentuating predominant time-frequency components, restraining adverse influence of impulsive components, and separating impulsive components. Simulation results were conducted to illustrate the advantages of the proposed method over the conventional Lomb periodogram in adaptive time-frequency resolution, sparse representation for sinusoids, robustness to impulsive components, and applicability to nonuniformly sampled data. Moreover, as the computation of the proposed method at each time sample and frequency is independent of others, parallel computing can be conveniently employed without much difficulty to significantly reduce the computational time of our proposed method for real-time applications. The proposed method is expected to find a wide range of applications in instrumentation and measurement and related areas. Its potential applications to power quality analysis and speech signal analysis are also discussed and demonstrated.en_US
dc.languageengen_US
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=19-
dc.relation.ispartofIEEE Transactions on Instrumentation and Measurementen_US
dc.subjectAdaptive window selection-
dc.subjectLomb periodogram-
dc.subjectM-estimation-
dc.subjectnonuniformly sampled data-
dc.subjectpower quality analysis-
dc.subjectregularization-
dc.subjecttime-frequency analysis (TFA)-
dc.subjectweighted least squares (WLS)-
dc.titleA New Regularized Adaptive Windowed Lomb Periodogram for Time-Frequency Analysis of Nonstationary Signals With Impulsive Componentsen_US
dc.typeArticleen_US
dc.identifier.emailZhang, Z:zgzhang@eee.hku.hken_US
dc.identifier.emailChan, SC:scchan@eee.hku.hken_US
dc.identifier.authorityZhang, Z=rp01565en_US
dc.identifier.authorityChan, SC=rp00094en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1109/TIM.2012.2186655en_US
dc.identifier.scopuseid_2-s2.0-84863988375-
dc.identifier.hkuros223267-
dc.identifier.hkuros225394-
dc.identifier.isiWOS:000306519900023-
dc.publisher.placeUnited Statesen_US
dc.identifier.scopusauthoridZhang, Z=8597618700en_US
dc.identifier.scopusauthoridChan, SC=13310287100en_US
dc.identifier.scopusauthoridWang, C=55021866100en_US
dc.identifier.issnl0018-9456-

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