Distributed compressed estimation based on compressive sensing

Songcen Xu; Rodrigo C. De Lamare; H. Vincent Poor

doi:10.1109/LSP.2015.2400372

Distributed compressed estimation based on compressive sensing

Songcen Xu, Rodrigo C. De Lamare, H. Vincent Poor

Research output: Contribution to journal › Article › peer-review

58 Scopus citations

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.

Original language	English (US)
Article number	7031884
Pages (from-to)	1311-1315
Number of pages	5
Journal	IEEE Signal Processing Letters
Volume	22
Issue number	9
DOIs	https://doi.org/10.1109/LSP.2015.2400372
State	Published - Sep 1 2015

All Science Journal Classification (ASJC) codes

Signal Processing
Electrical and Electronic Engineering
Applied Mathematics

Keywords

Compressive sensing
Sensor networks
distributed compressed estimation
measurement matrix optimization

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10.1109/LSP.2015.2400372

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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 = "Compressive sensing, Sensor networks, distributed compressed estimation, measurement matrix optimization",

author = "Songcen Xu and {De Lamare}, {Rodrigo C.} and Poor, {H. Vincent}",

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N2 - 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.

AB - 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.

KW - Compressive sensing

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KW - distributed compressed estimation

KW - measurement matrix optimization

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Distributed compressed estimation based on compressive sensing

Abstract

All Science Journal Classification (ASJC) codes

Keywords

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