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Passive Object Tracking Using MGEKF Algorithm

  • M. Kavitha Lakshmi
  • S. Koteswara Rao
  • K. Subrahmanyam
  • V. Gopi Tilak
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 701)

Abstract

This paper is mainly about the underwater object (Submarine) tracking as it plays a crucial role in maritime environment. Earlier many methods have been developed by using only bearing measurement which requires great computation time. The proposed method in the paper Modified Gain Extended Kalman Filter (MGEKF) focuses on the use of elevation measurement also in addition to bearing for tracking. This reduces the complexity in the detection of the object which is presented in the simulated results.

References

  1. 1.
    Song, T.L., Speyer, J.L.: A stochastic analysis of a modified gain extended kalman filter with application to estimation with bearings only measurements. IEEE Trans. Autom. Control 30(10), 940–949 (1985)CrossRefGoogle Scholar
  2. 2.
    Galkowski, P.J., Islam, M.A.: An alternative derivation of the modified gain function of Song and Speyer. IEEE Trans. Autom. Control 36(11), 1323–1326 Nov’ 1991MathSciNetCrossRefGoogle Scholar
  3. 3.
    Koteswara Rao, S.: Modified gain extended Kalman filter with application to angles only underwater passive target tracking. In: Proceedings of ICSP, pp. 1439–1442, Mar’ 1998Google Scholar
  4. 4.
    Wan, E.A., Van Der Merwe, R.: The unscented Kalman filter for non-linear estimation. In: Proceedings of Symposiums 2000 on Adaptive System for Signal Processing, Communication and Control. Canada Oct 2000Google Scholar
  5. 5.
    Omkar Lakshmi Jagan, B., Koteswara Rao, S., Jawahar, A., Karishma, S.K.B.: Application of Bar-Shalom and Fortmann’s input estimation for underwater target tracking. Indian J. Sci. Technol. (2016)Google Scholar
  6. 6.
    Ni, J., Wang, C., Fan, X., Yang, S.X.: Abioinspired neural model based extended Kalman filter for robot slam. (Research Article) (Report). Mathematical Problems in Engineering, Annual 2014 IssueGoogle Scholar
  7. 7.
    Koteswara Rao, S., RajaRajeswari, K., Lingamurthy, K.S.: Unscented Kalman filter with application to bearings-only target tracking. IETE J. Res. 55(2), 63–67 (2009)CrossRefGoogle Scholar
  8. 8.
    Koteswara Rao, S., Sunanda Babu, V.: Unscented Kalman filter with application to bearings-only passive maneuvering target tracking. In: IEEE International Conference on Signal processing, Communications and Networking, pp. 219–224 Jan’ 2008Google Scholar
  9. 9.
    Lalehbadriasl, kutluyildogamcay: Three-dimensional target motion analysis using azimuth/Elevation Angles. IEEE Trans. Aerosp. Electr. Syst. 50(4), 3178–3194 Oct’ 2014Google Scholar
  10. 10.
    Omkar Lakshmi Jagan, B., Koteswara Rao, S., Lakshmi Prasanna, K., Jawahar, A., Karishma, S.K.B.: Novel estimation algorithm for bearings-only target tracking. Int. J. Eng. Technol. 8(1), 238–246 Mar’ 2016Google Scholar
  11. 11.
    Omkar Lakshmi Jagan, B., Koteswara Rao, S., Jawahar, A., Karishma, S.K.B.: Unscented Kalman filter with application to bearing-only passive target tracking. Indian J. Sci. Technol. 9(19), 1–10 (2016)CrossRefGoogle Scholar
  12. 12.
    Ravi Kumar, D.V., Koteswara Rao, S.: Underwater bearings-only passive target tracking using estimate fusion technique. Adv. Mil. Technol. 10(2), 31–44, Dec’ 2015Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • M. Kavitha Lakshmi
    • 1
  • S. Koteswara Rao
    • 1
  • K. Subrahmanyam
    • 1
  • V. Gopi Tilak
    • 1
  1. 1.Koneru Lakshmaiah Education FoundationVaddeswaram, GunturIndia

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