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Sensor models and localization algorithms for sensor networks based on received signal strength
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0003-3270-171X
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Swedish Defence Research Agency, Linköping, Sweden.
2012 (English)In: EURASIP Journal on Wireless Communications and Networking, ISSN 1687-1472, E-ISSN 1687-1499, Vol. 1, no 16Article in journal (Refereed) Published
Abstract [en]

Received signal strength (RSS) can be used in sensor networks as a ranging measurement for positioning and localization applications. This contribution studies the realistic situation where neither the emitted power nor the power law decay exponent be assumed to be known. The application in mind is a rapidly deployed network consisting of a number of sensor nodes with low-bandwidth communication, each node measuring RSS of signals traveled through air (microphones) and ground (geophones). The first contribution concerns validation of a model in logarithmic scale, that is, linear in the unknown nuisance parameters (emitted power and power loss constant). The parameter variation is studied over time and space. The second contribution is a localization algorithm based on this model, where the separable least squares principle is applied to the non-linear least squares (NLS) cost function, after which a cost function of only the unknown position is obtained. Results from field trials are presented to illustrate the method, together with fundamental performance bounds. The ambition is to pave the way for sensor configuration design and more thorough performance evaluations as well as filtering and target tracking aspects.

Place, publisher, year, edition, pages
Springer, 2012. Vol. 1, no 16
Keyword [en]
sensor networks, localization, sensor models, acoustic sensors, seismic sensors, separable non-linear least squares
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-96722DOI: 10.1186/1687-1499-2012-16ISI: 000322169200005OAI: oai:DiVA.org:liu-96722DiVA: diva2:642959
Note

Funding Agencies|VINNOVA supported Centre for Advanced Sensors, Multisensors and Sensor Networks, FOCUS, at the Swedish Defence Research Agency, FOI||

Available from: 2013-08-23 Created: 2013-08-23 Last updated: 2017-12-13Bibliographically approved

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