Study on the Effect of the Sensor Array on the Source Localization Performance in Shallow Water

  • Phu Ninh TranEmail author
  • Khanh Dang Trinh
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 221)


In the paper, we investigate the effect of the total number of sensors on the localization performance in a shallow water area. The source localization performance is evaluated by using the White Noise Constraint (WNC) matching field processing (MFP) algorithm in this paper. The obtained results demonstrate that the quantity of the sensors influences on the accuracy of the localization performance that is estimated for the case of the fixed target as well as for the case of the moving one. The effect of the amount of the sensors studied on this paper can be used as guidelines to design sensor arrays in a particular shallow water area for a passive sonar system.


Matched field processing Source localization Shallow water 


  1. 1.
    Baggeroer, A.B., Kuperman, W.A., Mikhalevsky, P.N.: An overview of matched field methods in ocean acoustics. IEEE J. Oceanic Eng. 18(4), 401–424 (1993)CrossRefGoogle Scholar
  2. 2.
    Del Balzo, D.R., Feuillade, C., Rowe, M.M.: Effects of water-depth mismatch on matched-field localization in shallow water. J. Acoust. Soc. Am. 83(6), 2180–2185 (1988)CrossRefGoogle Scholar
  3. 3.
    Feuillade, C., Del Balzo, D.R., Rowe, M.M.: Environmental mismatch in shallow-water matched-field processing: geoacoustic parameter variability. J. Acoust. Soc. Am. 85(6), 2354–2364 (1989)CrossRefGoogle Scholar
  4. 4.
    Gebbie, J.T.: Advances in aquatic target localization with passive sonar (2014)Google Scholar
  5. 5.
    Harley, J.B., Mourn, J.M.F.: Matched field processing localization with random sensor topologies. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1404–1408. IEEE (2014)Google Scholar
  6. 6.
    Jensen, F.B., Kuperman, W.A., Porter, M.B., Schmidt, H.: Computational Ocean Acoustics. Modern Acoustics and Signal Processing. Springer, New York (2011). CrossRefzbMATHGoogle Scholar
  7. 7.
    Kolev, N.Z., Georgiev, G.D.: Reduced rank MVDR shallow water matched field processing for passive vertical sonar array source localization. In: 2007 15th International Conference on Digital Signal Processing, pp. 87–90. IEEE (2007)Google Scholar
  8. 8.
    Kolev, N.: Sonar Systems. InTech (2011)Google Scholar
  9. 9.
    Trinh, D.K., Tran, P.N., Nguyen, Q.T.: An investigation of the effects of factors on underwater localization with low source level in shallow water. Int. J. Adv. Eng. Technol. (IJAET) 9(3), 584–591 (2016)Google Scholar
  10. 10.
    Wang, Q., Jiang, Q.: Simulation of matched field processing localization based on empirical mode decomposition and Karhunen-Loeve expansion in underwater waveguide environment. EURASIP J. Adv. Sign. Process. 2010(1), 483–524 (2010)MathSciNetGoogle Scholar
  11. 11.
    Xiao, Z., Xu, W., Gong, X.: Robust matched field processing for source localization using convex optimization. In: Oceans 2009, Europe, pp. 1–5. IEEE (2009)Google Scholar
  12. 12.
    Zhang, K., Xu, W.: Performance of robust matched-field processing with convex optimization. In: OCEANS 2016, Shanghai, pp. 1–5. IEEE (2016)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  1. 1.Le Quy Don UniversityHanoiVietnam

Personalised recommendations