The sensor selection problem for bounded uncertainty sensing models

Volkan Isler, Ruzena Bajcsy

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

69 Scopus citations

Abstract

We address the problem of selecting sensors so as to minimize the error in estimating the position of a target. We consider a generic sensor model where the measurements can be interpreted as polygonal, convex subsets of the plane. This model applies to a large class of sensors including cameras. We present an approximation algorithm which guarantees that the resulting error in estimation is within a factor 2 of the least possible error. In establishing this result, we formally prove that a constant number of sensors suffice for a good estimate - an observation made by many researchers. In the second part of the paper, we study the scenario where the target's position is given by an uncertainty region and present algorithms for both probabilistic and online versions of this problem.

Original languageEnglish (US)
Title of host publication2005 Fourth International Symposium on Information Processing in Sensor Networks, IPSN 2005
Pages151-158
Number of pages8
Volume2005
DOIs
StatePublished - Dec 1 2005
Event4th International Symposium on Information Processing in Sensor Networks, IPSN 2005 - Los Angeles, CA, United States
Duration: Apr 25 2005Apr 27 2005

Other

Other4th International Symposium on Information Processing in Sensor Networks, IPSN 2005
CountryUnited States
CityLos Angeles, CA
Period4/25/054/27/05

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