Change search
ReferencesLink to record
Permanent link

Direct link
Sensor Selection with Correlated Noise
KTH, School of Electrical Engineering (EES), Automatic Control.
2010 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

We consider the problem of selecting k sensors out of m available (linear) sensors, so that the error in estimating some parameters is minimized. When the sensor noises are uncorrelated, the sensor selection problem can be (approximately) solved by a method recently suggested by Joshi and Boyd, which relies on a convex relaxation of the underlying combinatorial optimization problem. This thesis describes a non-trivial extension of the relaxation method to the case when the measurement noises are correlated, as occurs, for example, in a sensor scheduling problem in a dynamic system. We develop several new semidenite programming (SDP) relaxations for the problem, which give provable bounds on the attainable performance, as well as suboptimal sensor selections. Numerical experiments for sensor scheduling suggest that the methods work well.

Place, publisher, year, edition, pages
2010. , 18 p.
National Category
Control Engineering
URN: urn:nbn:se:kth:diva-105136OAI: diva2:570091
Subject / course
Automatic Control
Educational program
Master of Science in Engineering - Electrical Engineering
Available from: 2012-12-03 Created: 2012-11-16 Last updated: 2012-12-03Bibliographically approved

Open Access in DiVA

fulltext(175 kB)147 downloads
File information
File name FULLTEXT01.pdfFile size 175 kBChecksum SHA-512
Type fulltextMimetype application/pdf

By organisation
Automatic Control
Control Engineering

Search outside of DiVA

GoogleGoogle Scholar
Total: 147 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 90 hits
ReferencesLink to record
Permanent link

Direct link