Research Article
Wind Turbine Clutter Mitigation for Weather Radar by Extreme Learning Machine (ELM) Method
2 downloads
@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
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.
Copyright © 2020–2025 ICST
