An identification technique for data acquisition characterization in the presence of nonlinear distortions and time base distortions

The nonlinear behavior of data acquisition channels and analog-to-digital converters is often measured using sine-wave measurements. High-frequency sampling scopes also suffer from time base distortions. This implies that the signals are sampled at a nonequidistant time grid. This paper describes a robust and efficient identification technique to characterize acquisition channels which suffer from both nonlinear distortions and/or time base distortions in the presence of both additive and jitter noise. An automatic model selection scheme and the generation of uncertainty bounds are obtained through the statistical properties of the proposed simulator. The applicability of the method is demonstrated on both simulations and measurements of high-frequency sampling scopes.

[1]  Rik Pintelon,et al.  An improved sine-wave fitting procedure for characterizing data acquisition channels , 1995 .

[2]  Gerd Vandersteen,et al.  A sinewave fitting procedure for characterizing data acquisition channels in the presence of time base distortion and time jitter , 1996 .

[3]  Yves Rolain,et al.  Maximum likelihood estimation of errors-in-variables models using a sample covariance matrix obtained from small data sets , 1997 .

[4]  Gerd Vandersteen,et al.  Model selection through a statistical analysis of the global minimum of a weighted nonlinear least squares cost function , 1997, IEEE Trans. Signal Process..

[5]  Gerd Vandersteen,et al.  Maximum likelihood estimator for jitter noise models , 1999, IMTC/99. Proceedings of the 16th IEEE Instrumentation and Measurement Technology Conference (Cat. No.99CH36309).

[6]  Gerd Vandersteen,et al.  Frequency-domain system identification using non-parametric noise models estimated from a small number of data sets , 1997, Autom..

[7]  J. Cadzow,et al.  Signal processing via least squares error modeling , 1990, IEEE ASSP Magazine.

[8]  R. Fletcher Practical Methods of Optimization , 1988 .

[9]  J. P. Norton,et al.  An Introduction to Identification , 1986 .

[10]  Yves Rolain,et al.  Signal reconstruction for nonequidistant finite length sample sets: a "KIS" approach , 1998, IMTC/98 Conference Proceedings. IEEE Instrumentation and Measurement Technology Conference. Where Instrumentation is Going (Cat. No.98CH36222).

[11]  R. Jennrich Asymptotic Properties of Non-Linear Least Squares Estimators , 1969 .

[12]  J. Verspecht,et al.  Accurate spectral estimation based on measurements with a distorted-timebase digitizer , 1993, 1993 IEEE Instrumentation and Measurement Technology Conference.

[13]  Gerd Vandersteen,et al.  Maximum likelihood estimator for jitter noise models [HF sampling scopes] , 2000, IEEE Trans. Instrum. Meas..

[14]  Gerd Vandersteen,et al.  General framework for asymptotic properties of generalized weighted nonlinear least-squares estimators with deterministic and stochastic weighting , 1996, IEEE Trans. Autom. Control..

[15]  J. B. Rettig,et al.  Picosecond time interval measurements , 1995 .

[16]  Gerard N. Stenbakken,et al.  Time-base nonlinearity determination using iterated sine-fit analysis , 1998, IEEE Trans. Instrum. Meas..

[17]  Yves Rolain,et al.  Order estimation for linear time-invariant systems using frequency domain identification methods , 1997, IEEE Trans. Autom. Control..