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

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Abstract

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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Acknowledgments

The work presented in this paper is supported by a research grant (CityU 115612) from the Research Grants Council of the Government of the Hong Kong Special Administrative Region.

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Authors and Affiliations

  1. Department of Electronic Engineering, City University of Hong Kong, Kowloon Tong, Hong Kong

    Chi Sing Leung, Hing Cheung So & Frankie K. W. Chan

  2. Institute of Technology Management, National Chung Hsing University, Taichung, Taiwan

    John Sum

  3. Imperial College, London, UK

    Anthony G. Constantinides

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  1. Chi Sing Leung
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  2. John Sum
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  3. Hing Cheung So
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  4. Anthony G. Constantinides
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  5. Frankie K. W. Chan
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Correspondence to Chi Sing Leung.

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Leung, C.S., Sum, J., So, H.C. et al. Lagrange programming neural networks for time-of-arrival-based source localization. Neural Comput & Applic 24, 109–116 (2014). https://doi.org/10.1007/s00521-013-1466-z

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  • DOI: https://doi.org/10.1007/s00521-013-1466-z

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