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

系统工程与电子技术 ›› 2022, Vol. 44 ›› Issue (11): 3380-3387.doi: 10.12305/j.issn.1001-506X.2022.11.12

基于RCNN的双极化气象雷达天气信号检测

高涌荇¹, 王旭东^1,*, 汪玲¹, 朱岱寅¹, 郭军¹, 孟凡旺²

1. 南京航空航天大学雷达成像与微波光子技术教育部重点实验室, 江苏南京 210016
2. 中国航空工业集团公司雷华电子技术研究所, 江苏无锡 214063

收稿日期:2021-05-13 出版日期:2022-10-26 发布日期:2022-10-29
通讯作者: 王旭东
作者简介:高涌荇(1996—), 男, 硕士研究生, 主要研究方向为气象雷达目标识别|王旭东(1979—), 男, 副教授, 博士，主要研究方向为信号检测、参数估计、FPGA设计应用|汪玲(1977—), 女, 教授, 博士，主要研究方向为成像信号处理与智能信息处理|朱岱寅(1974—), 男, 教授, 博士, 主要研究方向为雷达信号处理、探测与成像、FPGA设计与应用和微小型雷达系统|郭军(1987—), 男, 博士研究生, 主要研究方向为图像理解与解译|孟凡旺(1982—), 男, 高级工程师, 博士，主要研究方向为机载雷达系统设计及信号处理
基金资助:
国家自然科学基金(61801212);工信部民机专项(MJ-2018-S-28)

Weather signal detection for dual polarization weather radar based on RCNN

Yongxing GAO¹, Xudong WANG^1,*, Ling WANG¹, Daiyin ZHU¹, Jun GUO¹, Fanwang MENG²

1. Key Laboratory of Radar Imaging and Microwave Photonics of the Ministry of Education, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
2. Leihua Electronic Technology Institute, Aviation Industry Corporation of China, Wuxi 214063, China

Received:2021-05-13 Online:2022-10-26 Published:2022-10-29
Contact: Xudong WANG

摘要/Abstract

摘要：

为检测混杂在地杂波、生物杂波中的天气信号, 提高定量降水精度, 提出了基于残差卷积神经网络(residual convolutional neural network, RCNN)的天气信号检测算法。首先, 将采集的极化参数水平反射率因子、差分反射率、相关系数、差分相移率堆叠为三维数组后进行预处理, 将其分为天气信号与杂波信号。然后, 开发并优化RCNN, 给出详细的网络结构。最后, 通过多次实际的降水过程对所提算法的检测效果进行评价。结果表明, 相比支持向量机以及卷积神经网络(convolutional neural network, CNN), 所提算法对天气信号的检测效果更好, 并且在不同仰角以及全年的实测数据上均表现出良好的检测性能。

关键词: 双极化气象雷达, 残差卷积神经网络, 天气信号检测, 深度学习

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

中图分类号:

TN959.4

高涌荇, 王旭东, 汪玲, 朱岱寅, 郭军, 孟凡旺. 基于RCNN的双极化气象雷达天气信号检测[J]. 系统工程与电子技术, 2022, 44(11): 3380-3387.

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.

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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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基于RCNN的双极化气象雷达天气信号检测

Weather signal detection for dual polarization weather radar based on RCNN

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