基于Level-II數(shù)據(jù)和模糊邏輯推理的氣象雷達風(fēng)電場雜波檢測與識別方法
doi: 10.11999/JEIT161031
基金項目:
國家自然科學(xué)基金委員會與中國民航局聯(lián)合資助項目(U1533110, 61571422)，中國民用航空局空中交通管理局科技計劃項目，中央高校基金(3122015D005)
Weather Radar Wind Farms Clutters Detection and Identification Method Based on Level-II Data and Fuzzy Logic Inference
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)
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摘要: 風(fēng)電場雜波具有強散射性和由于其葉片旋轉(zhuǎn)導(dǎo)致的頻譜展寬特性，其雷達回波很難用傳統(tǒng)的雜波濾波器濾除，進而導(dǎo)致氣象目標探測過程中的誤檢測與誤識別，這是影響新一代氣象雷達探測性能的一個重要因素。該文通過分析風(fēng)電場雜波區(qū)別于氣象目標的回波特性，基于氣象雷達二次產(chǎn)品(Level-II)實測數(shù)據(jù)選取某些特征參量，通過構(gòu)造特征量的概率分布直方圖和1維值域分布確定用于識別風(fēng)電場雜波的各個特征量的隸屬度函數(shù)，并設(shè)置相應(yīng)的邏輯規(guī)則，利用模糊邏輯推理系統(tǒng)(FIS)實現(xiàn)風(fēng)電場雜波的自適應(yīng)檢測與識別。通過采集幾組典型的Level-II數(shù)據(jù)對所提方法進行測試與驗證，均較為準確地識別出存在于氣象雷達視野內(nèi)的風(fēng)電場雜波，實驗結(jié)果證明了該文算法的可靠性。Abstract: 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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Key words:
- Weather radar /
- Wind farm /
- Clutter detection /
- Feature extraction /
- Fuzzy logic
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