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Multimedia Technology and Enhanced Learning. Second EAI International Conference, ICMTEL 2020, Leicester, UK, April 10-11, 2020, Proceedings, Part II

Research Article

Wind Turbine Clutter Mitigation for Weather Radar by Extreme Learning Machine (ELM) Method

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  • @INPROCEEDINGS{10.1007/978-3-030-51103-6_41,
        author={Mingwei Shen and Xu Yao and Di Wu and Daiyin Zhu},
        title={Wind Turbine Clutter Mitigation for Weather Radar by Extreme Learning Machine (ELM) Method},
        proceedings={Multimedia Technology and Enhanced Learning. Second EAI International Conference, ICMTEL 2020, Leicester, UK, April 10-11, 2020, Proceedings, Part II},
        proceedings_a={ICMTEL PART 2},
        year={2020},
        month={7},
        keywords={Weather radar Extreme Learning Machine Clutter suppression},
        doi={10.1007/978-3-030-51103-6_41}
    }
    
  • Mingwei Shen
    Xu Yao
    Di Wu
    Daiyin Zhu
    Year: 2020
    Wind Turbine Clutter Mitigation for Weather Radar by Extreme Learning Machine (ELM) Method
    ICMTEL PART 2
    Springer
    DOI: 10.1007/978-3-030-51103-6_41
Mingwei Shen1,*, Xu Yao1, Di Wu2, Daiyin Zhu2
  • 1: College of Computer and Information Engineering, Hohai University
  • 2: Key Laboratory of Radar Imagine and Microwave Photonics, Nanjing University of Aeronautics and Astronautics
*Contact email: smw_hhu1981@163.com

Abstract

Because of its overall performance, the Extreme Learning Machine (ELM) has been very concerned. This paper introduces the ELM algorithm into the clutter mitigation for weather radar, and proposes a wind turbine clutter mitigation method. Firstly, building training samples. Secondly, the model parameters for ELM are examined and optimized aim to improve its overall performance. Finally, the optimized ELM algorithm is used to recover the weather signal of the contaminated range bin. Simulation results show that the proposed algorithm can realize the precise recovery of the weather signal.

Keywords
Weather radar Extreme Learning Machine Clutter suppression
Published
2020-07-19
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-030-51103-6_41
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