Recursive consistent estimation with bounded noise

Sundeep Rangan; Vivek K. Goyal

doi:10.1109/18.904562

Recursive consistent estimation with bounded noise

Sundeep Rangan, Vivek K. Goyal

Electrical and Computer Engineering

Research output: Contribution to journal › Article › peer-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 language	English (US)
Pages (from-to)	457-464
Number of pages	8
Journal	IEEE Transactions on Information Theory
Volume	47
Issue number	1
DOIs	https://doi.org/10.1109/18.904562
State	Published - 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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10.1109/18.904562

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AB - 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.

KW - Consistent reconstruction

KW - Dithered quantization

KW - Frames

KW - Overcomplete representations

KW - Overdetermined linear equations

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Recursive consistent estimation with bounded noise

Abstract

Keywords

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