A New Time Domain Technique for Velocity Measurements Using Doppler Ultrasound

Barber, William J.; Eberhard, Jeffrey W.; Karr, S. G.

doi:10.1109/tbme.1985.325531

Cited by 101 publications

(29 citation statements)

References 10 publications

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“…Information of interest on Doppler signals may be found, for example, in [6][7][8][9][10][11]. Signals used here are issued from a flow measurement apparatus that uses pulsated emitting source with a constant Pulse Repetition Frequency (sampling frequency) PRF = 8 kHz.…”

Section: Experimental Signals: Doppler Velocimetrymentioning

confidence: 99%

“…Signals used here are issued from a flow measurement apparatus that uses pulsated emitting source with a constant Pulse Repetition Frequency (sampling frequency) PRF = 8 kHz. A fluid runs at constant or quasi-constant speed and the concern here is to measure its mean speed [6,9]. The Doppler mean frequency 3.3 f  is related to the mean velocity of the flow by (see [10] among above references),…”

Section: Experimental Signals: Doppler Velocimetrymentioning

confidence: 99%

“…Moreover, none of the above methods is suitable for extraction of such buried signals. In real world situations, non-ergodic processes in which a desired signal is buried may occur as shown by Doppler velocimetry measurements [6][7][8][9][10][11] used in this work.…”

Section: Introductionmentioning

confidence: 99%

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Extraction of Signals Buried in Noise: Non-Ergodic Processes

Bey¹

2010

IJCNS

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In this paper, we propose extraction of signals buried in non-ergodic processes. It is shown that the proposed method extracts signals defined in a non-ergodic framework without averaging or smoothing in the direct time or frequency domain. Extraction is achieved independently of the nature of noise, correlated or not with the signal, colored or white, Gaussian or not, and locations of its spectral extent. Performances of the proposed extraction method and comparative results with other methods are demonstrated via experimental Doppler velocimetry measurements.

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Section: Experimental Signals: Doppler Velocimetrymentioning

confidence: 99%

Section: Experimental Signals: Doppler Velocimetrymentioning

confidence: 99%

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Extraction of Signals Buried in Noise: Non-Ergodic Processes

Bey¹

2010

IJCNS

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“…By applying this principle to a truncated version of (9), we obtain ML estimates of the SM's given by (11) (12) The same concept applies to the ML spectral width estimator. Hence (13) Expressions (11)- (13) are exact ML estimates (under the bandlimited and negligible assumptions).…”

Section: B Maximum-likelihood Estimatementioning

confidence: 99%

Nonparametric estimation of mean Doppler and spectral width

Dias

Leitao

2000

IEEE Trans. Geosci. Remote Sensing

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Abstract-This paper proposes a new nonparametric method for estimation of spectral moments of a zero-mean Gaussian process immersed in additive white Gaussian noise. Although the technique is valid for any order moment, particular attention is given to the mean Doppler (first moment) and to the spectral width (square root of the centered second-spectral moment). By assuming that the power spectral density (PSD) of the underlying process is bandlimited, the maximum-likelihood estimates of its spectral moments are derived. A suboptimal estimate based on the sample covariance is also studied. Both methods are robust in the sense that they do not rely on any assumption concerning the PSD (besides being bandlimited). Under weak conditions, the set of estimates based on sample covariance is unbiased and strongly consistent. Compared with the classical pulse pair and the periodogram-based estimators, the proposed methods exhibit better statistical properties for asymmetric spectra and/or spectra with large spectral widths, while involving a computational burden of the same order.

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“…From the Doppler compression of the backscattered signal from the moving red blood cells, the velocity of the blood can be estimated. This may be done with either the conventional [1][2][3] technique, based on phaseshift measurement ͑PW-psm 4 at the same time maintain the wide bandwidth of the transmitted signal, several types of coded transmission signals have been devised, such as random noise 9,10 and pseudorandom noise. 11,12 A common element in these approaches is the use of phase-shift measurements in the signal processing.…”

Section: Introductionmentioning

confidence: 99%

Analytical and experimental comparisons between the frequency-modulated–frequency-shift measurement and the pulsed-wave–time-shift measurement Doppler systems

Wilhjelm

Pedersen

1996

The Journal of the Acoustical Society of America

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In previous publications, a new echo-ranging Doppler system based on transmission of repetitive coherent frequency-modulated (FM) sinusoids in two different implementations was presented. One of these implementations, the frequency-modulated–frequency-shift measurement (FM–fsm) Doppler system is, in this paper, compared with its pulsed-wave counterpart, the pulsed-wave–time-shift measurement (PW–tsm) Doppler system. When using transmitted PW and FM signals with a Gaussian envelope, the parallelism between the two systems can be stated explicitly and comparison can be made between the main performance indices for the two Doppler systems. The performance of the FM and PW Doppler systems is evaluated by means of numerical simulation and measurements of actual flow profiles. The results indicate that the two Doppler systems have very similar levels of performance.

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A New Time Domain Technique for Velocity Measurements Using Doppler Ultrasound

Cited by 101 publications

References 10 publications

Extraction of Signals Buried in Noise: Non-Ergodic Processes

Extraction of Signals Buried in Noise: Non-Ergodic Processes

Nonparametric estimation of mean Doppler and spectral width

Analytical and experimental comparisons between the frequency-modulated–frequency-shift measurement and the pulsed-wave–time-shift measurement Doppler systems

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