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Extreme learning machine terrain-based navigation for unmanned aerial vehicles

  • Extreme Learning Machine’s Theory & Application
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Abstract

Unmanned aerial vehicles (UAVs) rely on global positioning system (GPS) information to ascertain its position for navigation during mission execution. In the absence of GPS information, the capability of a UAV to carry out its intended mission is hindered. In this paper, we learn alternative means for UAVs to derive real-time positional reference information so as to ensure the continuity of the mission. We present extreme learning machine as a mechanism for learning the stored digital elevation information so as to aid UAVs to navigate through terrain without the need for GPS. The proposed algorithm accommodates the need of the on-line implementation by supporting multi-resolution terrain access, thus capable of generating an immediate path with high accuracy within the allowable time scale. Numerical tests have demonstrated the potential benefits of the approach.

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Acknowledgments

The authors gratefully acknowledge the funding provided by Temasek Defence Systems Institute, Singapore.

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Authors and Affiliations

  1. Intelligent Systems Center, Nanyang Technological University, Singapore, Singapore

    Ee May Kan & Meng Hiot Lim

  2. School of Computer Engineering, Nanyang Technological University, Singapore, Singapore

    Yew Soon Ong & Ah Hwee Tan

  3. Center for Microwave and RF, National University of Singapore, Singapore, Singapore

    Swee Ping Yeo

Authors
  1. Ee May Kan
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  2. Meng Hiot Lim
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  3. Yew Soon Ong
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  4. Ah Hwee Tan
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  5. Swee Ping Yeo
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Correspondence to Ee May Kan.

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Kan, E.M., Lim, M.H., Ong, Y.S. et al. Extreme learning machine terrain-based navigation for unmanned aerial vehicles. Neural Comput & Applic 22, 469–477 (2013). https://doi.org/10.1007/s00521-012-0866-9

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  • DOI: https://doi.org/10.1007/s00521-012-0866-9

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