Distributed reduced-rank estimation based on joint iterative optimization in sensor networks

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper proposes a novel distributed reduced-rank scheme and an adaptive algorithm for distributed estimation in wireless sensor networks. The proposed distributed scheme is based on a transformation that performs dimensionality reduction at each agent of the network followed by a reduced-dimension parameter vector. A distributed reduced-rank joint iterative estimation algorithm is developed, which has the ability to achieve significantly reduced communication overhead and improved performance when compared with existing techniques. Simulation results illustrate the advantages of the proposed strategy in terms of convergence rate and mean square error performance.

Original languageEnglish (US)
Title of host publication2014 Proceedings of the 22nd European Signal Processing Conference, EUSIPCO 2014
PublisherEuropean Signal Processing Conference, EUSIPCO
Pages2360-2364
Number of pages5
ISBN (Electronic)9780992862619
StatePublished - Jan 1 2014
Event22nd European Signal Processing Conference, EUSIPCO 2014 - Lisbon, Portugal
Duration: Sep 1 2014Sep 5 2014

Other

Other22nd European Signal Processing Conference, EUSIPCO 2014
CountryPortugal
CityLisbon
Period9/1/149/5/14

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering

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  • Cite this

    Xu, S., De Lamare, R. C., & Poor, H. V. (2014). Distributed reduced-rank estimation based on joint iterative optimization in sensor networks. In 2014 Proceedings of the 22nd European Signal Processing Conference, EUSIPCO 2014 (pp. 2360-2364). [6952852] European Signal Processing Conference, EUSIPCO.