Skip to main content
SpringerLink
Log in

Performance evaluation of a non-linear error model for underwater range computation utilizing GPS sonobuoys

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Deployed from an airborne platform or a surface vessel, arrays of GPS sonobuoys can be used to efficiently track and localize submarines. The range of the target of interest can be monitored with the deployed sonobuoys. However, the accuracy deteriorates when the target is on the detection range of only one sonobuoy. The objective of this research is to improve the range computation of the target of interest by establishing a non-linear error model for range error using adaptive neuro-fuzzy inference systems (ANFIS), which has the capabilities of dealing with data of high level of uncertainty and the advantage of being based on neural computation. Furthermore, the performance of the proposed model is examined with both experimental real field data and contact-level simulation data considering different scenarios for both the array of GPS sonobuoys and the target. The results discuss merits and the limitations of the proposed method.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. Burdic WS (1984) Underwater acoustic system analysis. ISBN: 0139367160; 978-013936716-8

  2. Korenberg MJ (1989) A robust orthogonal algorithm for system identification and time-series analysis. Biol Cybern 60(4):267–276. doi:10.1007/BF00204124

    Article  MATH  MathSciNet  Google Scholar 

  3. Farina A, Hanle E (1983) Position accuracy in netted monostatic and bistatic radar. IEEE Trans Aerosp Electron Syst 19(4):513–520

    Article  Google Scholar 

  4. de Gastines X (1996) Multi-Ping active classification for shallow water operations. NURC Rep. SR-250

  5. McIntyre M, Wang J, Kelly L (1999) The effect of position uncertainty in multistatic acoustic localization. In: Proceedings of the international conference on information, decision, and control, Adelaide, Australia, Feb 1999, pp 347–352

  6. Sandys-Wunsch M, Hazen M (2002) Multistatic localization error due to receiver positioning errors. IEEE J Ocean Eng 27(2):328–334

    Article  Google Scholar 

  7. Whitehouse HJ, Alsup JM, Leese de Escobar A, Sullivan SF (2004) A GPS sonobuoy localization system. In: Proceedings of IEEE PLANS 2004, 414–417. http://ieeexplore.ieee.org/ielx5/9147/29049/01309024.pdf?tp=. April 26–29

  8. Urick RJ (1996) Principles of underwater sound, xiii edn. McGraw-Hill, New York, p 384

    Google Scholar 

  9. Karim S, Mohsen M, Mohammadreza B (2008) Centralized and decentralized process and sensor fault monitoring using data fusion based on adaptive extended Kalman filter algorithm. Meas: J Int Meas Confed 41(10):1059–1076. doi:10.1016/j.measurement.2008.02.009

    Google Scholar 

  10. Ross TJ (2004) Fuzzy logic with engineering applications. Wiley, West Sussex

    MATH  Google Scholar 

  11. Roger Jang J-S (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Transaction on System, Man and Cybernetics, V23(3) pp 665–685, http://ieeexplore.ieee.org/search/wrapper.jsp?arnumber=256541

  12. Prabir B, Edmond C, Deborah N (1994) A cross-validatory method for dependent data. Biometrika V81(2):351–358. doi:10.1093/biomet/81.2.351

    Google Scholar 

  13. Noureldin A, El-Shafie A, El-Shiemy N (2007) Adaptive neuro-fuzzy module for inertial navigation system/global positioning system integration utilising position and velocity updates with real-time cross-validation. IET Radar Sonar Navig 1(5):388–396

    Article  Google Scholar 

  14. Noureldin A, El-Shafie A, Reda Taha M (2007) Optimizing neuro-fuzzy modules for data fusion of vehicular navigation systems using temporal cross-validation. Eng Appl Artif Intell V20(1):49–61

    Article  Google Scholar 

  15. Lang T, Dunne D, Mellema G (2009) An assessment of hierarchical data fusion using SEABAR’07 data. 12th international conference on information fusion, July 6–9

  16. Hempel C, Pacheco J (2009) Performance analysis of the probabilistic multi-hypothesis tracking algorithm on the SEABAR data sets. 12th international conference on information fusion, July 6–9

Download references

Acknowledgments

This research is funded in part by (1) the Department of National Defense through the Aerospace Engineering Research Advisory Committee and by the Natural Science and Engineering Research Council—NSERC for second and third authors, (2) the Smart Engineering System, University Kebangsaan Malaysia, Malaysia. The SEABAR’07 sea trial was held by the NATO Undersea Research Centre (NURC). The SEABAR’07 data were made available under agreement with NURC and Defence Research and Development Canada—Atlantic.

Author information

Authors and Affiliations

  1. Civil and Structural Engineering Department, University Kebangsaan Malaysia, UKM, Bangi, Malaysia

    Ahmed El-Shafie

  2. Department of Geomatics Engineering, University of Calgary, Calgary, AB, Canada

    Abdalla Osman

  3. Department of Electrical and Computer Engineering, Royal Military College, Kingston, ON, Canada

    Aboelmagd Noureldin

  4. Electric, Electronics Systems Engineering Department, University Kebangsaan Malaysia, UKM, Bangi, Malaysia

    Aini Hussain

Authors
  1. Ahmed El-Shafie
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abdalla Osman
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Aboelmagd Noureldin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Aini Hussain
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ahmed El-Shafie.

Rights and permissions

Reprints and permissions

About this article

Cite this article

El-Shafie, A., Osman, A., Noureldin, A. et al. Performance evaluation of a non-linear error model for underwater range computation utilizing GPS sonobuoys. Neural Comput & Applic 19, 1057–1067 (2010). https://doi.org/10.1007/s00521-010-0340-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-010-0340-5

Keywords

Search

Navigation