A Target Tracking System for ASV Collision Avoidance Based on the PDAF

  • Erik F. Wilthil
  • Andreas L. Flåten
  • Edmund F. Brekke
Chapter
Part of the Lecture Notes in Control and Information Sciences book series (LNCIS, volume 474)

Abstract

Safe navigation and guidance of an autonomous surface vehicle (ASV) depends on automatic sensor fusion methods capable of discovering static and moving obstacles in the vicinity of the ASV. A key component in such a system is a method for target tracking . In this paper, we report a complete radar tracking system based on the classical probabilistic data association filter (PDAF) . The tracking system is tested on real radar data recorded in Trondheimsfjorden, Norway.

Notes

Acknowledgements

This work was supported by the Research Council of Norway through the projecs 223254 (Centre for Autonomous Marine Operations and Systems at NTNU) and the project 244116/O70 (Sensor Fusion and Collision Avoidance for Autonomous Marine Vehicles). The authors would like to express great gratitude to Kongsberg Maritime and Maritime Robotics for placing high-grade navigation technology and the Telemetron vehicle at our disposal, and especially Thomas Ingebretsen for help with implementing the software interfaces.

References

  1. 1.
    Bar-Shalom, Y., Li, X.R.: Multitarget-Multisensor Tracking: Principles and Techniques. YBS Publishing, England (1995)Google Scholar
  2. 2.
    Bar-Shalom, Y., Li, X.R., Kirubarajan, T.: Estimation with Applications to Tracking and Navigation: Theory Algorithms and Software. Wiley, New York (2001)Google Scholar
  3. 3.
    Brekke, E., Hallingstad, O., Glattetre, J.: The signal-to-noise ratio of human divers. In: Proceedings of OCEANS’10, Sydney, Australia (2010)Google Scholar
  4. 4.
    Challa, S., Morelande, M.R., Mušicki, D., Evans, R.J.: Fundamentals of Object Tracking. Cambridge University Press, Cambridge (2011)Google Scholar
  5. 5.
    Fossen, T.I.: Handbook of Marine Craft Hydrodynamics and Motion Control. Wiley, New York (2011)Google Scholar
  6. 6.
    Friedman, J.H., Bentley, J.L., Finkel, R.A.: An algorithm for finding best matches in logarithmic expected time. ACM Trans. Math. Softw. (TOMS) 3(3), 209–226 (1977)Google Scholar
  7. 7.
    Gandhi, P.P., Kassam, S.A.: Analysis of CFAR processors in non-homogeneous background. IEEE Trans. Aerosp. Electron. Syst. 24(4), 427–445 (1988)CrossRefGoogle Scholar
  8. 8.
    Harati-Mokhtari, A., Wall, A., Brooks, P., Wang, J.: Automatic identification system (AIS): data reliability and human error implications. J. Navig. 60(03), 373 (2007)CrossRefGoogle Scholar
  9. 9.
    Li, X.R., Jilkov, V.P.: Survey of maneuvering target tracking. Part I: dynamic models. IEEE Trans. Aerosp. Electron. Syst. 39(4), 1333–1364 (2003)Google Scholar
  10. 10.
    Mahler, R.: Statistical Multisource-Multitarget Information Fusion. Artech House, Norwood (2007)MATHGoogle Scholar
  11. 11.
    Muja, M., Lowe, D.G.: Scalable nearest neighbor algorithms for high dimensional data. IEEE Trans. Pattern Anal. Mach. Intell. 36(11), 2227–2240 (2014)Google Scholar
  12. 12.
    Niedfeldt, P.C., Beard, R.W.: Multiple target tracking using recursive RANSAC. In: American Control Conference (ACC), Portland, OR, USA, pp. 3393–3398 (2014)Google Scholar
  13. 13.
    Pulford, G.W.: Taxonomy of multiple target tracking methods. IEE Proc. Radar Sonar Navig. 152(5), 291–304 (2005)CrossRefGoogle Scholar
  14. 14.
    Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A.Y.: ROS: an open-source robot operating system. In: ICRA Workshop on Open Source Software. Kobe, Japan (2009)Google Scholar
  15. 15.
    Svec, P., Thakur, A., Raboin, E., Shah, B.C., Gupta, S.K.: Target following with motion prediction for unmanned surface vehicle operating in cluttered environments. Auton. Robots 36(4), 383–405 (2014)CrossRefGoogle Scholar
  16. 16.
    Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. MIT Press, Cambridge (2005)Google Scholar
  17. 17.
    Vo, B.-N., Mallick, M., Bar-Shalom, Y., Coraluppi, S., Osborne, R., Mahler, R., Vo, B.-T.: Multitarget tracking. Wiley Encyclopedia of Electrical and Electronics Engineering. Wiley, New York (2015)Google Scholar
  18. 18.
    Wolf, M.T., Assad, C., Kuwata, Y., Howard, A., Aghazarian, H., Zhu, D., Thomas, L., Trebi-Ollennu, A., Huntsberger, T.: 360-degree visual detection and target tracking on an autonomous surface vehicle. J. Field Robot. 27(6), 819–833 (2010)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Erik F. Wilthil
    • 1
  • Andreas L. Flåten
    • 1
  • Edmund F. Brekke
    • 1
  1. 1.Department of Engineering CyberneticsCentre for Autonomous Marine Operations and Systems, Norwegian University of Science and Technology (NTNU)TrondheimNorway

Personalised recommendations