Conference proceeding
Conditions for Guaranteed Convergence in Sensor and Source Localization
2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07, Vol.2, pp.II-1081-II-1084
04/2007
DOI: 10.1109/ICASSP.2007.366427
Abstract
This paper considers localization of a source or a sensor from distance measurements. We argue that linear algorithms proposed for this purpose are susceptible to poor noise performance. Instead given a set of sensors/anchors of known positions and measured distances of the source/sensor to be localized from them, we propose a potentially nonconvex weighted cost function whose global minimum estimates the location of the source/sensor one seeks. The contribution of this paper is to provide nontrivial ellipsoidal and polytopic regions surrounding these sensors/anchors of known positions, such that if the object to be localized is in this region localization occurs by globally convergent gradient descent. This has implication to the deployment of sensors/anchors to achieve a desired level of geographical coverage.
Details
- Title: Subtitle
- Conditions for Guaranteed Convergence in Sensor and Source Localization
- Creators
- Bariş Fidan - Australian National UniversitySoura Dasgupta - University of IowaBrian D O Anderson - Australian National University
- Resource Type
- Conference proceeding
- Publication Details
- 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07, Vol.2, pp.II-1081-II-1084
- DOI
- 10.1109/ICASSP.2007.366427
- ISSN
- 1520-6149
- eISSN
- 2379-190X
- Publisher
- IEEE
- Language
- English
- Date published
- 04/2007
- Academic Unit
- Electrical and Computer Engineering
- Record Identifier
- 9984197539402771
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