Measuring the noise cumulative distribution function using quantized data

Paolo Carbone, Johan Schoukens, I. Kollár, Antonio Moschitta

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

This paper considers the problem of estimating the cumulative distribution function and probability density function of a random variable using data quantized by uniform and non-uniform quantizers. A simple estimator is proposed based on the empirical distribution function that also takes the values of the quantizer transition levels into account. The properties of this estimator are discussed and analyzed at first by simulations. Then by removing all assumptions that are difficult to apply, a new procedure is described that does not require neither the transition levels nor the input sequence used to source the quantizer to be known. The experimental results obtained using a commercial 12-b data acquisition system show the applicability of this estimator to real-world type of problems.

Original languageEnglish
Article number7446304
Pages (from-to)1540-1546
Number of pages7
JournalIEEE Transactions on Instrumentation and Measurement
Volume65
Issue number7
DOIs
Publication statusPublished - Jul 1 2016

Keywords

  • Estimation
  • identification
  • nonlinear estimation problems
  • nonlinear quantizers
  • quantization.

ASJC Scopus subject areas

  • Instrumentation
  • Electrical and Electronic Engineering

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