Adaptive distributed compressed estimation based on recursive least squares with sensing matrix design

Huang Bai; Songcen Xu; Sheng Li; Rodrigo C. De Lamare; Xiongxiong He; H. Vincent Poor

doi:10.1109/ICASSP.2016.7472366

Adaptive distributed compressed estimation based on recursive least squares with sensing matrix design

Huang Bai, Songcen Xu, Sheng Li, Rodrigo C. De Lamare, Xiongxiong He, H. Vincent Poor

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Scopus citations

Abstract

In this paper, a distributed compressed estimation (DCE) scheme is presented based on a distributed recursive-least squares algorithm for sparse signals and systems along with a sensing matrix design procedure based on compressive sensing techniques. The D-CE scheme consists of compression and decompression modules inspired by compressive sensing to perform distributed compressed estimation. A design procedure is developed under the DCE framework and a novel algorithm is developed to optimize the sensing matrix, which can further improve the performance of the proposed DCE and distributed adaptive algorithms. Simulations for a wireless sensor network show the advantages of the proposed scheme and algorithm in terms of convergence rate and mean square error performance.

Original language	English (US)
Title of host publication	2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3691-3695
Number of pages	5
ISBN (Electronic)	9781479999880
DOIs	https://doi.org/10.1109/ICASSP.2016.7472366
State	Published - May 18 2016
Event	41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Shanghai, China Duration: Mar 20 2016 → Mar 25 2016

Publication series

Name	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	2016-May
ISSN (Print)	1520-6149

Other

Other	41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016
Country/Territory	China
City	Shanghai
Period	3/20/16 → 3/25/16

All Science Journal Classification (ASJC) codes

Software
Signal Processing
Electrical and Electronic Engineering

Keywords

Distributed compressed estimation
compressive sensing
sensing matrix design
sensor networks

Access to Document

10.1109/ICASSP.2016.7472366

Cite this

Bai, H., Xu, S., Li, S., De Lamare, R. C., He, X., & Poor, H. V. (2016). Adaptive distributed compressed estimation based on recursive least squares with sensing matrix design. In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings (pp. 3691-3695). Article 7472366 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2016-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2016.7472366

Bai, Huang ; Xu, Songcen ; Li, Sheng et al. / Adaptive distributed compressed estimation based on recursive least squares with sensing matrix design. 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 3691-3695 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

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title = "Adaptive distributed compressed estimation based on recursive least squares with sensing matrix design",

abstract = "In this paper, a distributed compressed estimation (DCE) scheme is presented based on a distributed recursive-least squares algorithm for sparse signals and systems along with a sensing matrix design procedure based on compressive sensing techniques. The D-CE scheme consists of compression and decompression modules inspired by compressive sensing to perform distributed compressed estimation. A design procedure is developed under the DCE framework and a novel algorithm is developed to optimize the sensing matrix, which can further improve the performance of the proposed DCE and distributed adaptive algorithms. Simulations for a wireless sensor network show the advantages of the proposed scheme and algorithm in terms of convergence rate and mean square error performance.",

keywords = "Distributed compressed estimation, compressive sensing, sensing matrix design, sensor networks",

author = "Huang Bai and Songcen Xu and Sheng Li and {De Lamare}, {Rodrigo C.} and Xiongxiong He and Poor, {H. Vincent}",

note = "Funding Information: This research was supported in part by the National Natural Science Foundation of China under Grants 61503339, 61273195 and 61473262, and in part by the U.S. National Science Foundation under Grants CNS-1456793 and ECCS-1343210. Publisher Copyright: {\textcopyright} 2016 IEEE.; 41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 ; Conference date: 20-03-2016 Through 25-03-2016",

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doi = "10.1109/ICASSP.2016.7472366",

language = "English (US)",

series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",

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Bai, H, Xu, S, Li, S, De Lamare, RC, He, X & Poor, HV 2016, Adaptive distributed compressed estimation based on recursive least squares with sensing matrix design. in 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings., 7472366, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2016-May, Institute of Electrical and Electronics Engineers Inc., pp. 3691-3695, 41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016, Shanghai, China, 3/20/16. https://doi.org/10.1109/ICASSP.2016.7472366

Adaptive distributed compressed estimation based on recursive least squares with sensing matrix design. / Bai, Huang; Xu, Songcen; Li, Sheng et al.
2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2016. p. 3691-3695 7472366 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2016-May).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - Adaptive distributed compressed estimation based on recursive least squares with sensing matrix design

AU - Bai, Huang

AU - Xu, Songcen

AU - Li, Sheng

AU - De Lamare, Rodrigo C.

AU - He, Xiongxiong

AU - Poor, H. Vincent

N1 - Funding Information: This research was supported in part by the National Natural Science Foundation of China under Grants 61503339, 61273195 and 61473262, and in part by the U.S. National Science Foundation under Grants CNS-1456793 and ECCS-1343210. Publisher Copyright: © 2016 IEEE.

PY - 2016/5/18

Y1 - 2016/5/18

N2 - In this paper, a distributed compressed estimation (DCE) scheme is presented based on a distributed recursive-least squares algorithm for sparse signals and systems along with a sensing matrix design procedure based on compressive sensing techniques. The D-CE scheme consists of compression and decompression modules inspired by compressive sensing to perform distributed compressed estimation. A design procedure is developed under the DCE framework and a novel algorithm is developed to optimize the sensing matrix, which can further improve the performance of the proposed DCE and distributed adaptive algorithms. Simulations for a wireless sensor network show the advantages of the proposed scheme and algorithm in terms of convergence rate and mean square error performance.

AB - In this paper, a distributed compressed estimation (DCE) scheme is presented based on a distributed recursive-least squares algorithm for sparse signals and systems along with a sensing matrix design procedure based on compressive sensing techniques. The D-CE scheme consists of compression and decompression modules inspired by compressive sensing to perform distributed compressed estimation. A design procedure is developed under the DCE framework and a novel algorithm is developed to optimize the sensing matrix, which can further improve the performance of the proposed DCE and distributed adaptive algorithms. Simulations for a wireless sensor network show the advantages of the proposed scheme and algorithm in terms of convergence rate and mean square error performance.

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KW - compressive sensing

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SP - 3691

EP - 3695

BT - 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

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Y2 - 20 March 2016 through 25 March 2016

ER -

Bai H, Xu S, Li S, De Lamare RC, He X, Poor HV. Adaptive distributed compressed estimation based on recursive least squares with sensing matrix design. In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2016. p. 3691-3695. 7472366. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). doi: 10.1109/ICASSP.2016.7472366

Adaptive distributed compressed estimation based on recursive least squares with sensing matrix design

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