Strategies for Distributed Sensor Selection Using Convex Optimization

Authors

F. Altenbach, S. Corroy, G. Böcherer, R. Mathar,

Abstract

        Consider the estimation of an unknown parameter vector in a linear measurement model. Centralized sensor selection consists in selecting a set of ks sensor measurements, from a total number of m potential measurements. The performance of the corresponding selection is measured by the volume of an estimation error covariance matrix. In this work, we consider the problem of selecting these sensors in a distributed or decentralized fashion. In particular, we study the case of two leader nodes that perform naive decentralized selections. We demonstrate that this can degrade the performance severely. Therefore, two heuristics based on convex optimization methods are introduced, where we first allow one leader to make a selection, and then to share a modest amount of information about his selection with the remaining node. We will show that both heuristics clearly outperform the naive decentralized selection, and achieve a performance close to the centralized selection.

BibTEX Reference Entry 

@inproceedings{AlCoBoMa12,
	author = {Fabian Altenbach and Steven Corroy and Georg B{\"o}cherer and Rudolf Mathar},
	title = "Strategies for Distributed Sensor Selection Using Convex Optimization",
	pages = "2391-2396",
	booktitle = "{IEEE} Global Communications Conference 2012 (GLOBECOM 2012)",
	address = {Anaheim, California, USA},
	month = Dec,
	year = 2012,
	hsb = hsb999910279073,
	}

Downloads

 Download paper  Download bibtex-file

This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights there in are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.