Distributed Compressed Estimation Based on Compressive Sensing

Author

Songcen Xu ; de Lamare, Rodrigo C. ; Poor, H. Vincent

Author_Institution

Dept. of Electron., Univ. of York, York, UK

Volume

22

Issue

9

fYear

2015

fDate

Sept. 2015

Firstpage

1311

Lastpage

1315

Abstract

This letter proposes a novel distributed compressed estimation scheme for sparse signals and systems based on compressive sensing techniques. The proposed scheme consists of compression and decompression modules inspired by compressive sensing to perform distributed compressed estimation. A design procedure is also presented and an algorithm is developed to optimize measurement matrices, which can further improve the performance of the proposed distributed compressed estimation scheme. Simulations for a wireless sensor network illustrate the advantages of the proposed scheme and algorithm in terms of convergence rate and mean square error performance.

Keywords

compressed sensing; convergence of numerical methods; data compression; mean square error methods; sparse matrices; wireless sensor networks; compression module; compressive sensing; convergence rate; decompression module; distributed compressed estimation; mean square error method; measurement matrix optimization; sparse signal; wireless sensor network; Algorithm design and analysis; Compressed sensing; Estimation; Optimization; Signal processing algorithms; Vectors; Wireless sensor networks; Compressive sensing; distributed compressed estimation; measurement matrix optimization; sensor networks;

fLanguage

English

Journal_Title

Signal Processing Letters, IEEE

Publisher

ieee

ISSN

1070-9908

Type

jour

DOI

10.1109/LSP.2015.2400372

Filename

7031884