Analog Neural Network Approach for Source Localization Using Time-of-Arrival Measurements

  • Chi-Sing Leung
  • H. C. So
  • Frankie K. W. Chan
  • A. G. Constantinides
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7664)


Source localization can be achieved by making use of the time-of-arrival (TOA) measurements, but it is not a trivial task because the TOAs have nonlinear relationships with the source coordinates. This paper exploits a neural network technique, namely, Lagrange programming neural networks, for TOA-based localization. We also investigate the local stability of our formulation. Simulation results demonstrate that the performance of the proposed location estimator approaches the optimality benchmark of Cram\({\rm\acute{e}}\)r-Rao lower bound.


Analog network source localization time-of-arrival 


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  1. 1.
    Stojmenovic, I.: Handbook of Sensor Networks: Algorithms and Architectures. Wiley, New York (2005)CrossRefGoogle Scholar
  2. 2.
    Huang, Y., Benesty, J. (eds.): Audio Signal Processing for Next-Generation Multimedia Communication Systems. Kluwer Academic Publishers (2004)Google Scholar
  3. 3.
    Liberti, J.C., Rappaport, T.S.: Smart Antennas for Wireless Communications: IS-95 and Third Generation CDMA Applications. Prentice-Hall (1999)Google Scholar
  4. 4.
    So, H.C.: Source localization: Algorithms and analysis. In: Zekavat, S.A., Buehrer, R.M. (eds.) Handbook of Position Location: Theory, Practice, and Advances. John Wiley & Sons, Inc. (2011)Google Scholar
  5. 5.
    Chen, J.C., Hudson, R.E., Yao, K.: Maximum-likelihood source localization and unknown sensor location estimation for wideband signals in the near field 50(8), 1843–1854 (2002)Google Scholar
  6. 6.
    Chan, Y.T., Ho, K.C.: A simple and efficient estimator for hyperbolic location. IEEE Transactions on Signal Processing 42(8), 1905–1915 (1994)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Hopfield, J.J.: Neural networks and physical systems with emergent collective computational abilities. Proc. of the National Academy of Sciences 79, 2554–2558 (1982)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Chua, L.O., Lin, G.N.: Nonlinear programming without computation. IEEE Trans. on Circuits Syst. 31, 182–188 (1984)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Gao, X.B.: Exponential stability of globally projected dynamics systems. IEEE Trans. Neural Networks 14, 426–431 (2003)CrossRefGoogle Scholar
  10. 10.
    Hu, X., Wang, J.: A recurrent neural network for solving a class of general variational inequalities. IEEE Trans. on Systems, Man, and Cybernetics, Part B: Cybernetics 37(3), 528–539 (2007)CrossRefGoogle Scholar
  11. 11.
    Sum, J., Leung, C.S., Tam, P., Young, G., Kan, W., Chan, L.W.: Analysis for a class of winner-take-all model. IEEE Trans. Neural Networks 10(1), 64–71 (1999)CrossRefGoogle Scholar
  12. 12.
    Wang, J.: Analysis and design of a k-winners-take-all model with a single state variable and the heaviside step activation function. IEEE Trans. Neural Networks 21(9), 1496–1506 (2010)CrossRefGoogle Scholar
  13. 13.
    Xiao, Y., Liu, Y., Leung, C.S., Sum, J., Ho, K.: Analysis on the convergence time of dual neural network-based kwta. IEEE Trans. Neural Networks and Learning Systems 23(4), 676–682 (2012)CrossRefGoogle Scholar
  14. 14.
    Zhang, S., Constantinidies, A.G.: Lagrange programming neural networks. IEEE Trans. on Circuits and Systems II 39, 441–452 (1992)zbMATHCrossRefGoogle Scholar
  15. 15.
    Caffery, J.J.: Wireless Location in CDMA Cellular Radio Systems. Kluwer Academic (2000)Google Scholar
  16. 16.
    Sprito, M.A.: On the accuracy of cellular mobile station location estimation. IEEE Trans. Veh. Technol. 50, 674–685 (2001)CrossRefGoogle Scholar
  17. 17.
    Torrieri, D.J.: Statistical theory of passive location systems. IEEE Trans. on Aerospace and Electronic Systems 20, 183–197 (1984)CrossRefGoogle Scholar
  18. 18.
    Cheung, K.W., Ma, W.-K., So, H.C.: Accurate approximation algorithm for TOA-based maximum likelihood mobile location using semidefinite programming. In: Proceedings of the International Conference on Acoustics, Speech, and Signal Processing, Montreal, Canada, vol. 2, pp. 145–148 (May 2004)Google Scholar
  19. 19.
    Biswas, P., Liang, T.-C., Toh, K.-C., Ye, Y., Wang, T.-C.: Semidefinite programming approaches for sensor network localization with noisy distance measurements. IEEE Transactions on Automation Science and Engineering 3(4), 360–371 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Chi-Sing Leung
    • 1
  • H. C. So
    • 1
  • Frankie K. W. Chan
    • 1
  • A. G. Constantinides
    • 2
  1. 1.Dept. of Electronic EngineeringCity University of Hong KongHong Kong
  2. 2.Imperial CollegeUK

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