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An Algorithm for Interpolating Ship Motion Vectors

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Języki publikacji
EN
Abstrakty
EN
Interpolation of ship motion vectors is able to be used for estimating the lost ship AIS dynamic information, which is important for replaying marine accidents and for analysing marine traffic data. The previous methods can only interpolate ship’s position, while not including ship's course and speed. In this paper, vector function is used to express the relationship between the ship’s time and space coordinates, and the tangent of the vector function and its change rate are able to express physical characteristics of ship’s course, speed and acceleration. The given AIS dynamic information can be applied to calculate the parameters of ship's vector function and then the interpolation model for ship motion vectors is developed to estimate the lost ship dynamic information at any given moment. Experiment results show that the ship motion vector function is able to depict the characteristics of ship motions accurately and the model can estimate not only the ship’s position but also ship’s course and speed at any given moment with limited differences.
Twórcy
autor
  • Merchant Marine College, Shanghai Maritime University, Shanghai, China
autor
  • Merchant Marine College, Shanghai Maritime University, Shanghai, China
autor
  • Merchant Marine College, Shanghai Maritime University, Shanghai, China
autor
  • Merchant Marine College, Shanghai Maritime University, Shanghai, China
Bibliografia
  • [1] Recommendation ITU‐R M.1371‐4, Technical characteristics for an automatic identification system using time‐division multiple access in the VHFmaritime mobile band 2010.4
  • [2] Shanghai Maritime Traffic Safety Index Real‐time Study Important Waters. Reports, Shanghai Maritime University 2011
  • [3] Perera L P,Oliveira P,Guedes Soares C. Maritime Traffic Monitoring Based on Vessel Detection, Tracking, State Estimation, and Trajectory Prediction. Intelligent Transportation Systems, IEEE Transactions on, 2012(13): 1188‐1200.
  • [4] LU Yan‐sheng , CHA Zhi‐yong , Pan Peng. An improve queed spatio‐temporal data model for moving objects. Journal of Hua Zhong University of Science and Technology: Natural Science,2006 (8): 32‐35.
  • [5] YU B KIM S H BAILEY T etal .Curve‐Based Representation of Moving Object Trajectories. IEEE International Database Engineering and Applications Symposium 2004 419‐425.
  • [6] YU KIM S H. Interpolating and Using Most Likely Trajectories in Moving‐Objects Databases. Database and Expert Systems Applications. Springer Berlin Heidelberg 2006 718~727.
  • [7] PACHECO R R,HOUNSELL M S,ROSSO R S U et al. Smooth Trajectory Tracking Interpolation on A Robot Simulator. Robotics Symposium and Intelligent Robotic Meeting (LARS) 2010 Latin American. IEEE 2010 13~18.
  • [8] LIU Z ZHAO J ZHANG L,et al. Realization of Mobile Robot Trajectory Tracking Control Based on Interpolation.Industrial Electronics, 2009. ISIE 2009.IEEE International Symposium on. IEEE, 2009: 648~651.
  • [9] Ling‐zhi S, Xin‐ping Y, Zhe M, et al. Restoring Method of Vessel Track Based on AIS Information. Distributed Computing and Applications to Business, Engineering & Science (DCABES), 2012 11th International Symposium on. IEEE, 2012: 336‐340.
  • [10] MONTENBRUCK O, GILL E. State Interpolation for On‐Board Navigation Systems. Aerospace Science and\ Technology,2001(5) 209‐220.
  • [11] YU B.A Spatiotemporal Uncertainty Model of Degree 1.5 for Continuously Changing Data Objects. Proceedings of the 2006 ACM Symposium on Applied Computing. ACM,2006: 1150‐1155.
  • [12] YU B KIM S H ALKOBAISI Set al. The Tornado Model: Uncertainty Model for Continuously Changing Data. Advances in Databases: Concepts, Systems and Applications. Springer Berlin Heidelberg 2007 624‐636.
  • [13] Pratt M J,FAUX I D.Computational Geometry for design and manufacture. Ellis Horwood Ltd, 1979.
  • [14] SU Bu‐qing ZHOU Ding‐yuan. Computation Geometry. Shanghai:Shanghai Science and Technology Press.1981.
Typ dokumentu
Bibliografia
Identyfikator YADDA
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