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An Evolutionary Vulnerability Detection Method for HFSWR Ship Tracking Algorithm

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Simulated Evolution and Learning (SEAL 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10593))

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Abstract

A high-frequency surface-wave radar (HFSWR) ship tracking algorithm’s performance is significantly affected by the dynamics of ships, in which track fragmentation can be frequently observed. However, it is still unclear about in which scenarios the dynamics of ships sabotages the tracking performance. In this paper, an evolutionary-based vulnerability detection method is proposed to automatically collect scenarios of different ship motion dynamics that can cause quantitative failures in a HFSWR ship tracking algorithm. Firstly, a grammar-based scenario model which can describe multiple types of temporal relationships and generate autonomous motion of any number of ships with comparatively low-dimension data is proposed. Secondly, an encoding scheme of scenario is proposed and corresponding grammar-guided genetic programming (GGGP) algorithm is designed to evolve scenarios that can sabotages the tracking performance. Results show the effectiveness of this method in evolving and collecting scenarios that can cause more serious track fragmentation in the tracking results, with insights into the vulnerability of ship tracking algorithm provided.

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Notes

  1. 1.

    A scenario is a description of a variety of possible futures. It can be represented as a simple sampling in the parameter space, or a complex story (i.e. a sequence of events) that describes the future.

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Acknowledgement

This work is financially supported by the National Natural Science Foundation of China (Project number: 61601428) and the National Science Foundation of China (Project number: 41506114).

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Authors and Affiliations

  1. College of Engineering, Ocean University of China, Qingdao, China

    Pengju Zhang, Kun Wang, Ling Zhang, Zexiao Xie & Liqin Zhou

Authors
  1. Pengju Zhang
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  2. Kun Wang
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  3. Ling Zhang
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  4. Zexiao Xie
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  5. Liqin Zhou
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Corresponding author

Correspondence to Kun Wang .

Editor information

Editors and Affiliations

  1. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

  2. City University of Hong Kong, Hong Kong, Kowloon, Hong Kong

    Kay Chen Tan

  3. Victoria University of Wellington, Wellington, Wellington, New Zealand

    Mengjie Zhang

  4. Southern University of Science and Technology, Shenzhen, China

    Ke Tang

  5. RMIT University, Melbourne, Victoria, Australia

    Xiaodong Li

  6. City University of Hong Kong, Kowloon Tong, Hong Kong

    Qingfu Zhang

  7. Peking University, Beijing, China

    Ying Tan

  8. University of Leipzig, Leipzig, Germany

    Martin Middendorf

  9. University of Surrey, Guildford, Surrey, United Kingdom

    Yaochu Jin

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Zhang, P., Wang, K., Zhang, L., Xie, Z., Zhou, L. (2017). An Evolutionary Vulnerability Detection Method for HFSWR Ship Tracking Algorithm. In: Shi, Y., et al. Simulated Evolution and Learning. SEAL 2017. Lecture Notes in Computer Science(), vol 10593. Springer, Cham. https://doi.org/10.1007/978-3-319-68759-9_62

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  • DOI: https://doi.org/10.1007/978-3-319-68759-9_62

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68758-2

  • Online ISBN: 978-3-319-68759-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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