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Neural Computing and Applications

Erschienen in:

01.01.2014 | ICONIP 2012

Lagrange programming neural networks for time-of-arrival-based source localization

verfasst von: Chi Sing Leung, John Sum, Hing Cheung So, Anthony G. Constantinides, Frankie K. W. Chan

Erschienen in: Neural Computing and Applications | Ausgabe 1/2014

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Finding the location of a mobile source from a number of separated sensors is an important problem in global positioning systems and wireless sensor networks. This problem can be achieved by making use of the time-of-arrival (TOA) measurements. However, solving this problem is not a trivial task because the TOA measurements have nonlinear relationships with the source location. This paper adopts an analog neural network technique, namely Lagrange programming neural network, to locate a mobile source. We also investigate the stability of the proposed neural model. Simulation results demonstrate that the mean-square error performance of our devised location estimator approaches the Cramér–Rao lower bound in the presence of uncorrelated Gaussian measurement noise.

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Titel: Lagrange programming neural networks for time-of-arrival-based source localization
verfasst von: Chi Sing Leung
John Sum
Hing Cheung So
Anthony G. Constantinides
Frankie K. W. Chan
Publikationsdatum: 01.01.2014
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 1/2014
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-013-1466-z

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