Evolutionary periodogram for nonstationary signals

Kayhan, Arda; El-Jaroudi, A.; Chaparro, Luıs F.

doi:10.1109/78.286968

Cited by 104 publications

(41 citation statements)

References 11 publications

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“…Many authors have proposed different approaches to modelling this kind of non-stationary signals, that can be classified: i) assuming that a non stationary process is locally stationary in a finite time interval so that various recursive estimation techniques (RLS, PLR, RIV, etc.) can be applied (Ljung, 1987); ii) a state space modelling and a Kalman filtering; iii) expanding each time-varying parameter coefficients onto a set of basis sequences (Charbonnier et al, 1987); and iv) nonparametric approaches for non-stationary spectrum estimation such a local evolving spectrum, STFT and WVD are also developed to characterize non-stationary signals (Kayhan et al, 1994). To overcome the drawbacks of the identification algorithms, wavelets could be also considered for time varying model identification.…”

Section: Wwwintechopencommentioning

confidence: 99%

Robust Audio Localization for Mobile Robots in Industrial Environments

Manzanares¹,

Bolea²,

Grau³

2011

Advances in Sound Localization

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Section: Wwwintechopencommentioning

confidence: 99%

Robust Audio Localization for Mobile Robots in Industrial Environments

Manzanares¹,

Bolea²,

Grau³

2011

Advances in Sound Localization

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“…Lx, Ly and Lz denote the dimensions of the length, width and height of the room with ideally rigid walls where the waves are reflected without loss, (1) is rewritten as [6]:…”

Section: Sound Model In a Closed Roommentioning

confidence: 99%

“…This expression represents a three dimensional stationary waves space in the room. Eigenfrequencies corresponding to (6) eigenvalues can be expressed by:…”

Section: Sound Model In a Closed Roommentioning

confidence: 99%

An identified LPV model for mobile robots navigation with audio features

Manzanares

Bolea

Grau

2010

IECON 2010 - 36th Annual Conference on IEEE Industrial Electronics Society

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Abstract-Non-speech audio is becoming more attractive to be used as features to mobile robots navigation in industrial environments. In this paper authors present their advances in determining robot's position in indoor spaces using as sound sources industrial machines. A novel model is build to locate the robot under different spaces. An identification process is used to obtain the LPV model and it is validated using a real robot. Some uncertainties due to the robot motion and other factors have been taken into account when determining the robot's position and the obtained results demonstrate the validity of the model.

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“…It allows a physically meaningful time-evolving spectral density function to be defined -with frequency having its usual meaning -for so-called "semistationary processes." In addition to Priestley, estimation of the ES has been condidered by many others [12], [17], [18], [22], [30], [31], [32]. (There have also been generalizations and modifications of the ES [1], [11], [34] directed to the analysis of deterministic signals and involving instantaneous frequency.…”

Section: Introductionmentioning

confidence: 99%

Statistical Properties for Coherence Estimators From Evolutionary Spectra

Walden

Cohen

2012

IEEE Trans. Signal Process.

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Abstract-Evolutionary spectra were developed by Priestley to extend spectral analysis to some nonstationary time series, in particularly semistationary processes, of which the ubiquitous uniformly modulated processes are a subclass. Coherence is well-defined for bivariate semistationary processes and can be estimated from such processes. We consider Priestley's estimator for the evolutionary spectral density matrix, and show that its elements can be written as weighted multitaper estimators with calculable weights and tapers. Under Gaussianity an approximating Wishartdistribution model follows for the spectral matrix, valid for all frequencies except small computable intervals near zero and Nyquist. Moreover, the critically important degrees of freedom are known. Consequently the statistical distribution of the coherence is given by Goodman's distribution and the raw coherence estimate can be accurately debiased. Theoretical results are verified using a model for wind fluctuations: simulations give excellent agreement between the mean debiased coherence estimates and true coherence, and between the proposed and empirical distributions of coherence.

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Evolutionary periodogram for nonstationary signals

Cited by 104 publications

References 11 publications

Robust Audio Localization for Mobile Robots in Industrial Environments

Robust Audio Localization for Mobile Robots in Industrial Environments

An identified LPV model for mobile robots navigation with audio features

Statistical Properties for Coherence Estimators From Evolutionary Spectra

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