Target recognition for coastal surveillance based on radar images and generalised Bayesian inference

Target recognition for coastal surveillance based on radar images and generalised Bayesian inference

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For coastal surveillance, this study proposes a novel approach to identify moving vessels from radar images with the use of a generalised Bayesian inference technique, namely the evidential reasoning (ER) rule. First of all, the likelihood information about radar blips is obtained in terms of the velocity, direction, and shape attributes of the verified samples. Then, it is transformed to be multiple pieces of evidence, which are formulated as generalised belief distributions representing the probabilistic relationships between the blip's states of authenticity and the values of its attributes. Subsequently, the ER rule is used to combine these pieces of evidence, taking into account their corresponding reliabilities and weights. Furthermore, based on different objectives and verified samples, weight coefficients can be trained with a non-linear optimisation model. Finally, two field tests of identifying moving vessels from radar images have been conducted to validate the effectiveness and flexibility of the proposed approach.


    1. 1)
      • 1. Kabakchiev, H., Behar, V., Garvanov, I., et al: ‘Experimental verification of maritime target parameter evaluation in forward scatter maritime radar’, IET Radar Sonar Navig., 2014, 9, (4), pp. 355363.
    2. 2)
      • 2. Ma, F., Wu, Q., Yan, X., et al: ‘Classification of automatic radar plotting aid targets based on improved fuzzy C-means’, Transp. Res. C, Emerg. Technol., 2015, 51, pp. 180195.
    3. 3)
      • 3. IEC I. 62388: ‘Maritime navigation and radio-communication equipment and systems, shipborne radar. Performance requirements, methods of testing and required test results’, 2013.
    4. 4)
      • 4. Faruq, A., Abdullah, S.S., Fauzi, M., et al: ‘Optimization of depth control for unmanned underwater vehicle using surrogate modeling technique’, IET Intell. Transp. Syst., 2011, 5, (3), pp. 197206.
    5. 5)
      • 5. Yang, J.B., Xu, D.L.: ‘A study on generalising Bayesian inference to evidential reasoning’, in Yang, J.B., Xu, D.L., Cuzzolin, F. (Eds.): ‘Belief functions: theory and applications’ (2004), pp. 180189.
    6. 6)
      • 6. Farrell, W.J.: ‘Interacting multiple model filter for tactical ballistic missile tracking’, IEEE Trans. Aerosp. Electron. Syst., 2008, 44, (2), pp. 418426.
    7. 7)
      • 7. Yoo, J.C., Kim, Y.S.: ‘Alpha–beta-tracking index (α–β–Λ) tracking filter’, Signal Process., 2003, 83, (1), pp. 169180.
    8. 8)
      • 8. He, X., Bi, Y., Guo, Y.: ‘Target tracking algorithm of ballistic missile in boost phase based on ground-based radar systems’, J. Inf. Comput. Sci., 2015, 12, (2), pp. 855864.
    9. 9)
      • 9. IEC I. 62288: ‘Maritime navigation and radio-communication equipment and systems – presentation of navigation-related information on shipborne navigational displays – general requirements, methods of testing and required test results’, 2014.
    10. 10)
      • 10. Zhou, D., Shen, X., Yang, W.: ‘Radar target recognition based on fuzzy optimal transformation using high-resolution range profile’, Pattern Recognit. Lett., 2013, 34, (3), pp. 256264.
    11. 11)
      • 11. Zhao, X., Wang, S., Zhang, J., et al: ‘Real-time fault detection method based on belief rule base for aircraft navigation system’, Chin. J. Aeronaut., 2013, 26, (3), pp. 717729.
    12. 12)
      • 12. Lee, D.J.: ‘Automatic identification of ARPA radar tracking vessels by CCTV camera system’, J. Korean Soc. Fisheries Technol., 2009, 45, (3), pp. 177187.
    13. 13)
      • 13. Xia, Y., Shi, X., Song, G., et al: ‘Towards improving quality of video-based vehicle counting method for traffic flow estimation’, Signal Process., 2016, 120, (c), pp. 672681.
    14. 14)
      • 14. Zhang, D., Yan, X.P., Yang, Z.L., et al: ‘Incorporation of formal safety assessment and Bayesian network in navigational risk estimation of the Yangtze river’, Reliab. Eng. Syst. Saf., 2013, 118, pp. 93105.
    15. 15)
      • 15. Li, B., Pang, F.W.: ‘An approach of vessel collision risk assessment based on the D–S evidence theory’, Ocean Eng., 2013, 74, pp. 1621.
    16. 16)
      • 16. Smarandache, F., Dezert, J., Tacnet, J.: ‘Fusion of sources of evidence with different importances and reliabilities’. Proc. of 13th Information Fusion (FUSION) Conf., Edinburgh UK, 26–29 July 2010, pp. 18.
    17. 17)
      • 17. Sun, S., Fu, G., Djordjević, S., et al: ‘Separating aleatory and epistemic uncertainties: probabilistic sewer flooding evaluation using probability box’, J. Hydrol., 2012, 420, pp. 360372.
    18. 18)
      • 18. Yang, J.B., Xu, D.L.: ‘ER rule for evidence combination’, Artif. Intell., 2013, 205, pp. 129.
    19. 19)
      • 19. Xu, X.B., Zhou, Z., Wang, C.L.: ‘Data fusion algorithm of fault diagnosis considering sensor measurement uncertainty’, Int. J. Smart Sens. Intell. Syst., 2013, 6, (1), pp. 171190.
    20. 20)
      • 20. Dempster, A.P.: ‘Upper and lower probabilities induced by a multivalued mapping’, Ann. Math. Stat., 1967, pp. 325339.
    21. 21)
      • 21. Nguyen, T., Sanner, S.: ‘Algorithms for direct 0–1 loss optimisation in binary classification’. Proc. of the 30th Int. Conf. on Machine Learning, Atlanta, Georgia, USA, 16–21 June 2013, pp. 10851093.
    22. 22)
      • 22. Lin, B., Huang, C.H.: ‘Comparison between ARPA radar and AIS characteristics for vessel traffic services’, J. Mar. Sci. Technol., 2006, 14, (3), pp. 182189.
    23. 23)
      • 23. Schmidhuber, J.: ‘Deep learning in neural networks: an overview’, Neural Netw., 2015, 61, pp. 85117.
    24. 24)
      • 24. Ma, F., Chen, Y.W., Yan, X.P., et al: ‘A novel marine radar targets extraction approach based on sequential images and Bayesian network’, Ocean Eng., 2016, 120, pp. 6477.

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