Book chapter
Realistic Anchor Positioning for Sensor Localization
Recent Advances in Learning and Control, pp.79-94
Lecture Notes in Control and Information Sciences, Springer London
2008
DOI: 10.1007/978-1-84800-155-8_6
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 non-convex 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 exponentially convergent gradient descent in the noise free case. Exponential convergence in the noise free case represents practical convergence as it ensures graceful performance degradation in the presence of noise. These results guide the deployment of sensors/anchors so that small subsets can be made responsible for practical localization in geographical areas determined by our approach.
Details
- Title: Subtitle
- Realistic Anchor Positioning for Sensor Localization
- Creators
- Bariş Fidan - Australian National UniversitySoura Dasgupta - University of IowaBrian D O Anderson - Australian National University
- Resource Type
- Book chapter
- Publication Details
- Recent Advances in Learning and Control, pp.79-94
- Publisher
- Springer London; London
- Series
- Lecture Notes in Control and Information Sciences
- DOI
- 10.1007/978-1-84800-155-8_6
- ISSN
- 0170-8643
- Language
- English
- Date published
- 2008
- Academic Unit
- Electrical and Computer Engineering
- Record Identifier
- 9984197181902771
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