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Volume 38 Issue 12
Jan.  2017
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HE Weikun, GUO Shuangshuang, WANG Xiaoliang, WU Renbiao. Weather Radar Wind Farms Clutters Detection and Identification Method Based on Level-II Data and Fuzzy Logic Inference[J]. Journal of Electronics & Information Technology, 2016, 38(12): 3252-3260. doi: 10.11999/JEIT161031
Citation: HE Weikun, GUO Shuangshuang, WANG Xiaoliang, WU Renbiao. Weather Radar Wind Farms Clutters Detection and Identification Method Based on Level-II Data and Fuzzy Logic Inference[J]. Journal of Electronics & Information Technology, 2016, 38(12): 3252-3260. doi: 10.11999/JEIT161031

Weather Radar Wind Farms Clutters Detection and Identification Method Based on Level-II Data and Fuzzy Logic Inference

doi: 10.11999/JEIT161031
Funds:

The National Natural Science Foundation Committee and the Civil Aviation Administration of China Jointly Funded Program (U1533110, 61571422), The Science and Technology Program of Air Traffic Management Bureau of Civil Aviation Administration of China, The Central College Fund Program (3122015D005)

  • Received Date: 2016-10-08
  • Rev Recd Date: 2016-11-16
  • Publish Date: 2016-12-19
  • Wind farms clutters have the characteristics of strong scattering and the Doppler spectrum spreading due to the blades rotation, the radar echoes can not be filtered out easily using the traditional ground clutter filter, hence causing the false detection and identification of meteorological targets in the process of target detection, which is an important influence factor on the new generation weather radar echoes. Based on the analysis of wind farms echoes characteristics distinguished from those of meteorological target echoes, some suitable feature parameters are chosen, and a robust good adaptive fuzzy logic system of wind farms clutters detection and identification is developed by using the secondary products (Level II) data and the Fuzzy Inference System (FIS), in which the membership functions of each feature parameters and the corresponding logical rules are defined by constructing probability distribution histogram and the one dimensional range distribution of the corresponding feature parameters. Several groups of typical Level II data are collected to test and verify the proposed method, the experimental results demonstrate the reliability of the proposed algorithm.
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