Modeling of time delay and its application to estimation of nonstationary delays

Chan, Y.T.; Riley, J.; Plant, J.

doi:10.1109/tassp.1981.1163620

Cited by 119 publications

(46 citation statements)

References 6 publications

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“…The interpolation of data between successive sample points consistent with a dynamic time delay has been developed by Chan et al, 6 and here we apply it to provide simulated oversampled density waveforms ñ 1 (t) and ñ 2 (t). The interpolated signal s i (t) is obtained from a discrete signal s(n) by 6…”

Section: Turbulent Velocity Simulation and Transfer Functionmentioning

confidence: 99%

Wavelet-based time-delay estimation for time-resolved turbulent flow analysis

Jakubowski

Fonck

Fenzi

et al. 2001

Review of Scientific Instruments

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A wavelet-transform-based spectral analysis is examined for application to beam emission spectroscopy ͑BES͒ data to extract poloidal rotation velocity fluctuations from the density turbulence data. Frequency transfer functions for a wavelet cross-phase extraction method are calculated. Numerical noise is reduced by shifting the data to give an average zero time delay, and the applicable frequency range is extended by numerical oversampling of the measured density fluctuations. This approach offers potential for direct measurements of turbulent transport and detection of zonal flows in tokamak plasma turbulence.

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Section: Turbulent Velocity Simulation and Transfer Functionmentioning

confidence: 99%

Wavelet-based time-delay estimation for time-resolved turbulent flow analysis

Jakubowski

Fonck

Fenzi

et al. 2001

Review of Scientific Instruments

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“…Basically, the ETDE has the same lter structure with the LMSTDE but the lter coe cients fw i (k)g are replaced by fsinc(i ;D e (k))g for ;P i P , whereD e (k) is the estimated delay. The output error function in the ETDE can be computed from e(k) = y(k) ;x(k ;D e (k)) wherex(k ;D e (k)) 4 In the absence of noise or if the lter length is in nitely long, then D e = D. H o wever, for other circumstances, the unbiased property of the ETDE will not necessarily exist. As a rule of thumb, the delay bias can be reduced by a p p r o ximately one-tenth by either increasing the SNR by 1 0 d B or by a ten-fold increase of P .…”

Section: B Ctdementioning

confidence: 99%

Comparative study of five LMS-based adaptive time delay estimators

Ching

2001

IEE Proc., Radar Sonar Navig.

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A n umber of adaptive algorithms for estimating time di erence between signals received at two spatially separated sensors have been proposed to model the delay using an FIR lter. Among them, there are the LMSTDE, CTDE, ETDE, SETDE and ETDGE, which are computationally e cient because of the LMS implementation. In this paper, these ve m e t h o d s are compared in terms of estimation accuracy and computational complexity. I t i s p r o ved that the LMSTDE and ETDGE attain identical performance for su ciently long lter lengths, although the ETDE and SETDE perform similarly to the ETDGE at high signal-to-noise ratio (SNR) and low SNR, respectively. The CTDE involves minimum computational load but it is the worst estimator in the presence of noise. In addition, optimum realizations of the LMSTDE as well as the ETDE and its variants are derived and their delay v ariances are compared with the Cram er-Rao lower bound. Simulation results show that ETDGE outperforms the other four methods for a wide range of lter lengths at di erent S N R s .

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“…Estimation of constant and time varying delay was considered in [4,5] , where Least Mean Square (LMS) adaptive filter was used to correlate the two input data. The resulting delay estimate was obtained as the location at which the filter obtained its peak value.…”

Section: Introductionmentioning

confidence: 99%

Neural Networks Based Time-Delay Estimation using DCT Coefficients

Shaltaf¹,

Mohamma²

2009

American J. of Applied Sciences

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This study dealt with the problem of estimating constant time delay embedded into a received signal that was noisy, delayed and damped image of a known reference signal. The received signal was filtered, normalized with respect to the peak value it achieved and then transformed by the Discrete Cosine Transform (DCT) into DCT coefficients. Those DCT coefficients that were most sensitive to time delay variations were selected and grouped to form the Reduced Discrete Cosine Transform Coefficients set (RDCTC). The time delays embedded in the filtered signals were efficiently encoded into those RDCTC sets. The RDCTC sets were applied to a pre trained multi layer feedforward Neural Network (NN), which computed the time-delay estimates. The network was initially trained with large sets of RDCTC vectors, in which each RDCTC vector corresponded to a signal delayed by a randomly selected constant time-delay. Using the RDCTC as input to the NN instead of the full length incoming signal itself resulted in a major reduction in the NN size. Accurate time delay estimates were obtained through simulation and compared against estimates obtained through classical cross-correlation technique.

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Modeling of time delay and its application to estimation of nonstationary delays

Cited by 119 publications

References 6 publications

Wavelet-based time-delay estimation for time-resolved turbulent flow analysis

Wavelet-based time-delay estimation for time-resolved turbulent flow analysis

Comparative study of five LMS-based adaptive time delay estimators

Neural Networks Based Time-Delay Estimation using DCT Coefficients

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