Recursive consistent estimation with bounded noise

Sundeep Rangan, Vivek K. Goyal

Research output: Contribution to journalArticlepeer-review

Abstract

Estimation problems with bounded, uniformly distributed noise arise naturally in reconstruction problems from over complete linear expansions with subtractive dithered quantization. We present a simple recursive algorithm for such bounded-noise estimation problems. The mean-square error (MSE) of the algorithm is "almost" ο(1/n 2), where n is the number of samples. This rate is faster than the ο(1/n) MSE obtained by standard recursive least squares estimation and is optimal to within a constant factor.

Original languageEnglish (US)
Pages (from-to)457-464
Number of pages8
JournalIEEE Transactions on Information Theory
Volume47
Issue number1
DOIs
StatePublished - Jan 2001

Keywords

  • Consistent reconstruction
  • Dithered quantization
  • Frames
  • Overcomplete representations
  • Overdetermined linear equations

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Library and Information Sciences

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