基于RCNN的双极化气象雷达天气信号检测

doi:10.12305/j.issn.1001-506X.2022.11.12

Abstract

Abstract:

To detect weather signals submerged in ground clutter and biological clutter and improve the accuracy of quantitative precipitation, a weather signal detection algorithm based on residual convolutional neural network (RCNN) is proposed. Firstly, the collected polarization parameters: horizontal reflectivity, differential reflectivity, correlation coefficient, and differential propagation phase constant stack into a three dimensional array which are then divided into weather signals and clutter signals for preprocessing. Then, RCNN is developed and optimized, and the detailed structure of network is provided. Finally, the detection effect of the proposed algorithm is evaluated through several actual precipitation processes. As demonstrated by testing results, compared to support vector machines and convolutional neural network (CNN), the proposed algorithm has better detection effects on different elevation angles and measured data throughout the year.

Key words: dual polarization weather radar, residual convolutional neural network (RCNN), weather signal detection, deep learning

CLC Number:

TN959.4

Yongxing GAO, Xudong WANG, Ling WANG, Daiyin ZHU, Jun GUO, Fanwang MENG. Weather signal detection for dual polarization weather radar based on RCNN[J]. Systems Engineering and Electronics, 2022, 44(11): 3380-3387.

Figures/Tables 11

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方法	训练率/%	准确率/%
SVM	8	97.61
CNN	8	98.52
RCNN	2	98.50
	4	99.05
	6	99.03
	8	99.22

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Weather signal detection for dual polarization weather radar based on RCNN

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