Full information from measured ADC test data using maximum likelihood estimation

László Balogh, István Kollár, Linus Michaeli, Ján Šaliga, Jozef Lipták

Research output: Contribution to journalArticle

16 Citations (Scopus)


ADC testing is often done using sine wave excitation (see e.g. IEEE standard 1241). A sine wave is fitted to the measured data in least squares sense, and the residuals are analyzed further. In recent papers, it has been recognized that even more (and more precise) information can be extracted by the solution of the maximum likelihood equations. This is an improvement to the usual three-parameter and four-parameter fits. In this paper practical implementation of this algorithm is suggested. Then, theoretical background is overviewed. Further investigations lead to the statement that the same principle can be extended to any measurement which uses an excitation signal which can be described with a few parameters. A candidate for this is an exponential signal, with three parameters: e.g. start value, end (steady-state) value, and time constant. The maximum likelihood (ML) equations yield a solution for these too, more accurate than least squares (LS) fitting. Reasonable approximations make the ML problem solvable in practice.

Original languageEnglish
Pages (from-to)164-169
Number of pages6
JournalMeasurement: Journal of the International Measurement Confederation
Issue number2
Publication statusPublished - Feb 1 2012



  • ADC testing
  • IEEE-1241
  • Least squares fit
  • Maximum likelihood approach

ASJC Scopus subject areas

  • Instrumentation
  • Electrical and Electronic Engineering

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