Conditions for Guaranteed Convergence in Sensor and Source Localization
-
Altmetric Citations
Fidan, Baris; Dasgupta, Soura; Anderson, Brian
Description
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...[Show more]
dc.contributor.author | Fidan, Baris | |
---|---|---|
dc.contributor.author | Dasgupta, Soura | |
dc.contributor.author | Anderson, Brian | |
dc.coverage.spatial | Honolulu Hawaii | |
dc.date.accessioned | 2015-12-08T22:46:03Z | |
dc.date.created | April 15-20 2007 | |
dc.identifier.isbn | 1424407281 | |
dc.identifier.uri | http://hdl.handle.net/1885/37978 | |
dc.description.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. | |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE Inc) | |
dc.relation.ispartofseries | IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2007) | |
dc.source | Proceedings of the 2007 IEEE International Conference on Acoustics, Speech, and Signal Processing | |
dc.subject | Keywords: Algorithms; Convergence of numerical methods; Distance measurement; Frequency estimation; Optimization; Signal noise measurement; Global convergence; Gradient descent; Nonconvex weighted costs; Source localization; Sensor networks Global convergence; Gradient descent; Localization; Optimization; Sensors | |
dc.title | Conditions for Guaranteed Convergence in Sensor and Source Localization | |
dc.type | Conference paper | |
local.description.notes | Imported from ARIES | |
local.description.refereed | Yes | |
dc.date.issued | 2007 | |
local.identifier.absfor | 090609 - Signal Processing | |
local.identifier.ariespublication | u3357961xPUB156 | |
local.type.status | Published Version | |
local.contributor.affiliation | Fidan, Baris, College of Engineering and Computer Science, ANU | |
local.contributor.affiliation | Dasgupta, Soura, University of Iowa | |
local.contributor.affiliation | Anderson, Brian, College of Engineering and Computer Science, ANU | |
local.description.embargo | 2037-12-31 | |
local.bibliographicCitation.startpage | 1081 | |
local.bibliographicCitation.lastpage | 1084 | |
local.identifier.doi | 10.1109/ICASSP.2007.366427 | |
dc.date.updated | 2015-12-08T10:57:33Z | |
local.identifier.scopusID | 2-s2.0-34547548960 | |
Collections | ANU Research Publications |
Download
File | Description | Size | Format | Image |
---|---|---|---|---|
01_Fidan_Conditions_for_Guaranteed_2007.pdf | 680.02 kB | Adobe PDF | Request a copy |
Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.
Updated: 17 November 2022/ Responsible Officer: University Librarian/ Page Contact: Library Systems & Web Coordinator