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A drifting trajectory prediction model based on object shape and stochastic motion features

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Abstract

There is a huge demand to develop a method for marine search and rescue (SAR) operators automatically predicting the most probable searching area of the drifting object. This paper presents a novel drifting prediction model to improve the accuracy of the drifting trajectory computation of the sea-surface objects. First, a new drifting kinetic model based on the geometry characteristics of the objects is proposed that involves the effects of the object shape and stochastic motion features in addition to the traditional factors of wind and currents. Then, a computer simulation-based method is employed to analyze the stochastic motion features of the drifting objects, which is applied to estimate the uncertainty parameters of the stochastic factors of the drifting objects. Finally, the accuracy of the model is evaluated by comparison with the flume experimental results. It is shown that the proposed method can be used for various shape objects in the drifting trajectory prediction and the maritime search and rescue decision-making system.

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References

  1. BREIVIK Ø., ALLEN A. and CHRISTOPHE M. et al. Wind-induced drift of objects at sea: The leeway field method[J]. Applied Ocean Research, 2011, 33(2): 100-109.

    Article  Google Scholar 

  2. HACKETT B., BREIVIK Ø. and WETTRE C. Ocean Weather Forecasting: An integrated view of oceanography [ M]. Berlin, Germany: Springer-Verlag, 2006, 507–524.

    Book  Google Scholar 

  3. GASTGIFVARS M., LAURI H. and SARKANEN A. et al. Modelling surface drifting of buoys during a rapidly- moving weather front in the Gulf of Finland, Baltic Sea[J]. Estuarine, Coastal and Shelf Science, 2006, 70(4): 567–576

    Article  Google Scholar 

  4. KOD S., MARTIN P. J. and ROWLEY C. D. et al. A real-time coastal ocean prediction experiment for MREA04[J]. Journal of Marine Systems, 2008, 69(1–2): 17–28

    Google Scholar 

  5. ULLMANN D. S., O’DONNELL J. and KOHUT J. Trajectory prediction using HF radar surface currents: Monte Carlo simulations of prediction uncertainties[J]. Journal of Geophysical Research, 2006, 111(C12): 1–14.

    Google Scholar 

  6. DANIEL P., JAN G. and CABIOC’H F. et al. Drift modeling of cargo containers[J]. Spill Science and Technology Bulletin, 2002, 7(5–6): 279–288

    Article  Google Scholar 

  7. ESSEN H. H., BREIVIK Ø. and GUNTHER H. et al. Comparison of remotely measured and modeled currents in coastal areas of Norway and Spain[J]. The Global Atmosphere-Ocean System, 2003, 9(1–2): 39–64

    Article  Google Scholar 

  8. MONBETA V., AILLIOTA P. and PREVOSTOB M. Survey of stochastic models for wind and sea state time series[J]. Probabilistic Engineering Mechanics, 2007, 22(2): 113–126

    Article  Google Scholar 

  9. VANDENBULCKE L., BECKERS J. M. and LENARTZ F. et al. Super-ensemble techniques: Application to surface drift prediction[J]. Progress in Oceanography, 2009, 82(3): 149–167

    Article  Google Scholar 

  10. RIXEN M., FERREIRA-COELHO E. Operational surface drift prediction using linear and non-linear hyperensemble statistics on atmospheric and ocean models[J]. Journal of Marine Systems, 2007, 65(1–4): 105–121

    Article  Google Scholar 

  11. DAVIDSON F., ALLEN A. and BRASSINGTON G. et al. Applications of GODAE ocean current forecasts to search and rescue and ship routing[J]. Oceanography, 2009, 22(3): 176–181

    Article  Google Scholar 

  12. EIDE M., ENDRESEN Ø. and BREIVIK Ø. et al. Prevention of oil spill from shipping by modelling of dynamic risk[J]. Marine Pollution Bulletin, 2007, 54(10): 1619–1633.

    Article  Google Scholar 

  13. HONG S. P., CHO S. J. and PARK M. J. et al. Optimal search-relocation trade-off in Markovian-target searching[ J]. Computers and Operations Research, 2009, 36(6): 2097–2104

    Article  Google Scholar 

  14. NI Z., QIU Z. and SU T. On predicting boat drift for search and rescue[J]. Ocean Engineering, 2010, 37(13): 1169–1179.

    Article  Google Scholar 

  15. BREIVIK Ø., ALLEN A. An operational search and rescue model for the norwegian sea and the north sea[J]. Journal of Marine Systems, 2008, 69(1–2): 99–113

    Article  Google Scholar 

  16. ISOBE A., HINATA H. and KAKO S. et al. Interdisciplinary studies on environmental chemistry marine environmental modeling and analysis [M]. Tokyo, Japan: TERRAPUB, 2011, 239–249.

    Google Scholar 

  17. BREIVIK Ø., SATRA Ø. Real time assimilation of HF radar currents into a coastal ocean model[J]. Journal of Marine Systems, 2001, 28(3–4): 161–182

    Article  Google Scholar 

  18. ABDALLA S., CAVALERI L. Effect of wind variability and variable air density on wave modeling[J]. Journal of Geophysical Research. 2002, 107 (C7): 1–17

    Article  Google Scholar 

  19. NIE H. B. MCMC-based drifting trajectory prediction model and dynamic optimizing ship routeing algorithm[ D]. Master Thesis, Shanghai, China: Shanghai Maritime University, 2013(in Chinese).

    Google Scholar 

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Correspondence to Sheng-zheng Wang  (王胜正).

Additional information

Project supported by the National Natural Science Foundation of China (Grant Nos. 31100672, 51379121 and 61304230), the Shanghai Key Technology Plan Project (Grant Nos. 12510501800, 13510501600).

Biography: WANG Sheng-zheng (1976-), Male, Ph. D., Associate Professor

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Wang, Sz., Nie, Hb. & Shi, Cj. A drifting trajectory prediction model based on object shape and stochastic motion features. J Hydrodyn 26, 951–959 (2014). https://doi.org/10.1016/S1001-6058(14)60104-9

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  • DOI: https://doi.org/10.1016/S1001-6058(14)60104-9

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