An algorithm for the removal of noise and jitter in signals and its application to picosecond electrical measurement

Cox, M G; Harris, P M; Humphreys, D.A.

doi:10.1007/bf02108665

Cited by 11 publications

(20 citation statements)

References 4 publications

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“…Unlike in the classical estimation framework, we have a prior on , which allows us to formulate the minimum mean-square error (MMSE) estimator as the posterior expectation (12) The posterior distribution depends on the likelihood function , which can be expressed as in [4] as a product of marginal likelihoods: (13) As neither the likelihood nor posterior distribution has a simple closed form, the majority of this paper is devoted to approximating these functions using numerical and stochastic methods.…”

Section: A Problem Formulationmentioning

confidence: 99%

“…The effects of jitter on linear MMSE reconstruction of bandlimited signals are discussed in [10] and extended to the asymptotic case and multidimensional signals in [11]. More recently, [12] uses a second-order Taylor series approximation to perform weighted least-squares fitting of a jittered random signal. In [13], two postprocessing methods are described for the case when the sample times are discrete (on a dense grid).…”

Section: B Related Workmentioning

confidence: 99%

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Bayesian Post-Processing Methods for Jitter Mitigation in Sampling

Weller

Goyal

2011

IEEE Trans. Signal Process.

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Abstract-Minimum mean-square error (MMSE) estimators of signals from samples corrupted by jitter (timing noise) and additive noise are nonlinear, even when the signal parameters and additive noise have normal distributions. This paper develops a stochastic algorithm based on Gibbs sampling and slice sampling to approximate the optimal MMSE estimator in this Bayesian formulation. Simulations demonstrate that this nonlinear algorithm can improve significantly upon the linear MMSE estimator, as well as the EM algorithm approximation to the maximum likelihood (ML) estimator used in classical estimation. Effective off-chip postprocessing to mitigate jitter enables greater jitter to be tolerated, potentially reducing on-chip ADC power consumption.

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Section: A Problem Formulationmentioning

confidence: 99%

Section: B Related Workmentioning

confidence: 99%

Bayesian Post-Processing Methods for Jitter Mitigation in Sampling

Weller

Goyal

2011

IEEE Trans. Signal Process.

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“…In this case, techniques considering both, noise and jitter, must be applied, as e.g. the Median Method (Paulter and Larson, 2005), spline based interpolating algorithms (Cox et al, 1993), or an approach of deconvolving time jitter from the measured waveform (Gans, 1983;Verspecht, 1994). The time base unit used in the presented receiver is depicted in detail in Fig.…”

Section: Equivalent Time (Et) Sampling Receivermentioning

confidence: 99%

A time domain spherical near-field measurement facility for UWB antennas employing a hardware gating technique

et al. 2010

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A spherical near-field antenna measurement facility employing a time domain hardware gating technique is presented. On-off keyed sinusoidal impulses are used as stimuli requiring wideband antennas with a bandwidth in excess of 400 MHz. The received signal is evaluated in the time interval after reaching the steady state and before multipath components arising in the non-ideal anechoic chamber distort the signal.An application specific pulse generator synthesizing sinusoidal impulses with a sub-nanosecond settling time and a low-cost equivalent time (ET) sampling receiver developed and optimized for this particular purpose are described.Measurement results of typical ultra-wideband (UWB) antennas show a significant improvement of the measured antenna pattern compared to conventional techniques.Published by Copernicus Publications on behalf of the URSI Landesausschuss in der Bundesrepublik Deutschland e.V.

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“…In a regression spline model, one can model a signal without having a closed form analytical model for the signal. Our work is inspired by an earlier attempt to estimate the RMS value of jitter noise in high-speed sampled signals using a regression spline method [20]. For a clear discussion of regression spline modeling and related techniques, we direct readers to [21].…”

Section: Introductionmentioning

confidence: 99%

“…For a clear discussion of regression spline modeling and related techniques, we direct readers to [21]. In [20], repeated measurements of jittered signals provided estimates of the sample variance of the signal for each time sample. Based on an estimate of the derivative of the noise-free signal provided by a regression spline model, and the sample variance of the signal at each time sample, they estimated the RMS value of the timing jitter noise.…”

Section: Introductionmentioning

confidence: 99%

Adaptive characterization of jitter noise in sampled high-speed signals

Coakley

Wang

Hale

et al. 2003

IEEE Trans. Instrum. Meas.

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We estimate the root-mean-square (RMS) value of timing jitter noise in simulated signals similar to measured highspeed sampled signals. The simulated signals are contaminated by additive noise, timing jitter noise, and time shift errors. Before estimating the RMS value of the jitter noise, we align the signals (unless there are no time shift errors) based on estimates of the relative shifts from cross-correlation analysis. We compute the mean and sample variance of the aligned signals based on repeated measurements at each time sample. We estimate the derivative of the noise-free signal based, in part, on a regression spline fit to the average of the aligned signals. Our initial estimate of the RMS value of the jitter noise depends on estimated derivatives and sample variances at time samples where the magnitude of the estimated derivative exceeds a selected threshold. This initial estimate is generally biased. Using a parametric bootstrap approach, we adaptively adjust this initial estimate of the RMS value of the jitter noise based on an estimate of this bias. We apply our method to real data collected at NIST. We study how results depend on the derivative threshold.

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An algorithm for the removal of noise and jitter in signals and its application to picosecond electrical measurement

Cited by 11 publications

References 4 publications

Bayesian Post-Processing Methods for Jitter Mitigation in Sampling

Bayesian Post-Processing Methods for Jitter Mitigation in Sampling

A time domain spherical near-field measurement facility for UWB antennas employing a hardware gating technique

Adaptive characterization of jitter noise in sampled high-speed signals

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