Optimized Sensor Collaboration for Estimation of Temporally Correlated Parameters

Sijia Liu, Swarnendu Kar, Makan Fardad, Pramod K. Varshney

Research output: Contribution to journalArticle

13 Scopus citations

Abstract

In this paper, we aim to design the optimal sensor collaboration strategy for the estimation of time-varying parameters, where collaboration refers to the act of sharing measurements with neighboring sensors prior to transmission to a fusion center. We begin by addressing the sensor collaboration problem for the estimation of uncorrelated parameters. We show that the resulting collaboration problem can be transformed into a special nonconvex optimization problem, where a difference of convex functions carries all the nonconvexity. This specific problem structure enables the use of a convex-concave procedure to obtain a near-optimal solution. When the parameters of interest are temporally correlated, a penalized version of the convex-concave procedure becomes well suited for designing the optimal collaboration scheme. In order to improve computational efficiency, we further propose a fast algorithm that scales gracefully with problem size via the alternating direction method of multipliers. Numerical results are provided to demonstrate the effectiveness of our approach and the impact of parameter correlation and temporal dynamics of sensor networks on estimation performance.

Original languageEnglish (US)
Article number7572989
Pages (from-to)6613-6626
Number of pages14
JournalIEEE Transactions on Signal Processing
Volume64
Issue number24
DOIs
StatePublished - Dec 15 2016

Keywords

  • ADMM
  • convex-concave procedure
  • distributed estimation
  • semidefinite programming
  • sensor collaboration
  • wireless sensor networks

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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