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Link to original content: https://doi.org/10.1007/s00521-013-1466-z
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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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References

  1. Ilyas M, Mahgoub I (2005) Handbook of sensor networks: compact wireless and wired sensing systems. CRC Press, London

    Google Scholar 

  2. Stojmenovic I (2005) Handbook of sensor networks: algorithms and architectures. Wiley, New York

    Book  Google Scholar 

  3. Huang Y, Benesty J (2004) Audio signal processing for next-generation multimedia communication systems. Kluwer, Dordrecht

    Book  Google Scholar 

  4. Liberti JC, Rappaport TS (1999) Smart antennas for wireless communications: IS-95 and third generation CDMA applications. Prentice-Hall, Englewood Cliffs

    Google Scholar 

  5. Jagoe A (2003) Mobile location services: the definitive guide. Prentice Hall, Englewood Cliffs

    Google Scholar 

  6. Federal Communications Commission (1996) Revision of the Commission’s Rules to Ensure Compatibility with Enhanced 911 Emergency Calling Systems, RM-8143, CC Docket No.94-102

  7. So HC (2011) Source localization: algorithms and analysis. In: Zekavat SA, Buehrer RM (eds) Handbook of position location: theory, practice, and advances, Wiley, New York

    Google Scholar 

  8. Chen JC, Hudson RE, Yao K (2002) Maximum-likelihood source localization and unknown sensor location estimation for wideband signals in the near field. IEEE Trans Signal Process 50(8):1843–1854

    Article  Google Scholar 

  9. Chan YT, Ho KC (1994) A simple and efficient estimator for hyperbolic location. IEEE Trans Signal Process 42(8):1905–1915

    Article  MathSciNet  Google Scholar 

  10. Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci 79:2554–2558

    Article  MathSciNet  Google Scholar 

  11. Chua LO, Lin GN (1984) Nonlinear programming without computation. IEEE Trans Circuits Syst 31:182–188

    Article  MathSciNet  Google Scholar 

  12. Gao XB (2003) Exponential stability of globally projected dynamics systems. IEEE Trans Neural Netw 14:426–431

    Article  Google Scholar 

  13. Hu X, Wang J (2007) A recurrent neural network for solving a class of general variational inequalities. IEEE Trans Syst Man Cybern Part B Cybern 37(3):528–539

    Article  Google Scholar 

  14. Sum J, Leung CS, Tam P, Young G, Kan WK, Chan LW (1999) Analysis for a class of winner-take-all model. IEEE Trans Neural Netw 10(1):64–71

    Article  Google Scholar 

  15. Wang J (2010) Analysis and design of a k-winners-take-all model with a single state variable and the heaviside step activation function. IEEE Trans Neural Netw 21(9):1496–1506

    Article  Google Scholar 

  16. Xiao Y, Liu Y, Leung CS, Sum J, Ho K (2012) Analysis on the convergence time of dual neural network-based kwta. IEEE Trans Neural Netw Learn Syst 23(4):676–682

    Article  Google Scholar 

  17. Zhang S, Constantinidies AG (1992) Lagrange programming neural networks. IEEE Trans Circuits Syst II 39:441–452

    Article  MATH  Google Scholar 

  18. Caffery JJ (2000) Wireless location in CDMA cellular radio systems. Kluwer, Dordrecht

    Google Scholar 

  19. Sprito MA (2001) On the accuracy of cellular mobile station location estimation. IEEE Trans Veh Technol 50:674–685

    Article  Google Scholar 

  20. Torrieri DJ (1984) Statistical theory of passive location systems. IEEE Trans Aerosp Electron Syst 20:183–197

    Article  Google Scholar 

  21. Cheung KW, Ma WK, So HC (2004) Accurate approximation algorithm for TOA-based maximum likelihood mobile location using semidefinite programming. Proc Int Conf Acoust Speech Signal Process 2:145–148. Montreal, Canada

    Google Scholar 

  22. Biswas P, Liang TC, Toh KC, Ye Y, Wang TC (2006) Semidefinite programming approaches for sensor network localization with noisy distance measurements. IEEE Trans Autom Sci Eng 3(4):360–371

    Article  Google Scholar 

Download references

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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