Missing Data Recovery Via a Nonparametric Iterative Adaptive Approach

Author

Stoica, Petre ; Li, Jian ; Ling, Jun

Author_Institution

Dept. of Inf. Technol., Uppsala Univ., Uppsala

Volume

16

Issue

4

fYear

2009

fDate

4/1/2009 12:00:00 AM

Firstpage

241

Lastpage

244

Abstract

We introduce a missing data recovery methodology based on a weighted least squares iterative adaptive approach (IAA). The proposed method is referred to as the missing-data IAA (MIAA) and it can be used for uniform or nonuniform sampling as well as for arbitrary data missing patterns. MIAA uses the IAA spectrum estimates to retrieve the missing data, by means of either a frequency domain or a time domain approach. Numerical examples are presented to show the effectiveness of MIAA for missing data reconstruction. In particular, we show that MIAA can outperform an existing competitive approach, and this at a much lower computational cost.

Keywords

data handling; expectation-maximisation algorithm; information retrieval; iterative methods; mean square error methods; missing data reconstruction; missing data recovery; nonuniform sampling; time domain approach; weighted least squares iterative adaptive approach; Clustering algorithms; Councils; Extrapolation; Interpolation; Iterative methods; Least squares approximation; Least squares methods; Nonuniform sampling; Phase estimation; Sampling methods; Iterative adaptive approach; minimum mean-squared error; missing data recovery; spectral estimation; weighted least squares;

fLanguage

English

Journal_Title

Signal Processing Letters, IEEE

Publisher

ieee

ISSN

1070-9908

Type

jour

DOI

10.1109/LSP.2009.2014114

Filename

4781950