Acoustic Target Localization Algorithm in Wireless Sensor Networks

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 163)


Self-organizing and high fault tolerant characteristics of wireless sensor networks make them have great advantages in target tracking region, but untagged target localization is always a difficult problem to be solved. When the target appeared in the detection region, it must cause the nearby environment parameter change. This paper we use microphone to gather target signal. Much work has been done to improve the location accuracy with the effect of noise. In this paper UTLA target location method based on the signal transmission model has been proposed. The algorithm makes nodes calculate the target position cooperatively. Several experiments are made to verify the UTLA algorithm. The experimental results show that the target within sensor networks has better location result.


Wireless sensor networks Target localization UTLA algorithm 


  1. 1.
    Viani F, Lizzi L, Rocca P, Benedetti M, Donelli M, Massa A (2008) Object tracking through RSSI measurements in wireless sensor networks. Electron Lett 44(10):17–24CrossRefGoogle Scholar
  2. 2.
    Sheng XH, Hu YH (2004) Maximum likelihood multiple-source localization using acoustic energy measure measurement with wireless sensor networks. IEEE Trans Signal Process 53(1):44–53Google Scholar
  3. 3.
    Nguyen X, Jordan M, Sinopoli B (2005) A kernel-based learning approach to Ad-hoc sensor network localization. ACM Trans Sensor Netw 1:134–152CrossRefGoogle Scholar
  4. 4.
    Chen J, Hudson R, Yao K (2001) A maximum likelihood parametric approach to source localization. In: Proceedings IEEE international conference on acoustics, speech, and signal processing. IEEE Press, Washington, DC, vol 1, pp 1043–1046Google Scholar
  5. 5.
    Gu D, Wang Z (2008) Distributed regression over sensor networks: a support vector machine approach. In: Proceedings IEEE/RSJ international conference on intelligent robots and systems, vol 22, pp 3286–3291Google Scholar
  6. 6.
    Predd J, Kulkarni S, Poor H (2006) Distributed learning in wireless sensor networks. IEEE Signal Process Mag 23(4):56–69Google Scholar
  7. 7.
    Olama M, Djouadi S, Pendley C (2006) Position and velocity tracking in mobile cellular networks using the particle filter. In: IEEE wireless communication and networking conference. IEEE Press, Washington, DC, vol 4, pp 2261–2266Google Scholar
  8. 8.
    Olfati-Saber R (2007) Distributed Kalman filtering for sensor networks. In: Proceeding the 46th conference on decision and control. IEEE Press, Washington, DC, vol 32, pp 5492–5498Google Scholar
  9. 9.
    Liang Song (2007) Dimitrios Hatzinakas. A cross-layer architecture of wireless sensor networks for target tacking. IEEE Trans Netw 7:55–69Google Scholar
  10. 10.
    Shen X, Wang Z, Sun Y (2004) Wireless sensor networks for industrial applications. In: Proceedings of the world congress on intelligent control and automation (WCICA). IEEE Press, Washington, DC, pp 3636–3640Google Scholar
  11. 11.
    Proakis JG (1995) Digital communication, 3rd edn. McGraw-Hill, New YorkGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.School of Computer Sciences and TechnologyChina University of Mining & TechnologyXuzhouChina

Personalised recommendations