利用改进的GoogLeNet深度学习模型识别COSMIC-2掩星信号中的反射信号

罗文杰; 项杰; 杜华栋

doi:10.3878/j.issn.1006-9895.2202.21096

利用改进的GoogLeNet深度学习模型识别COSMIC-2掩星信号中的反射信号

doi: 10.3878/j.issn.1006-9895.2202.21096

国防科技大学气象海洋学院, 长沙410008

基金项目: 国家自然科学基金项目41475021

详细信息

作者简介:
罗文杰，男，1997年出生，硕士研究生，研究方向是人工智能方法在卫星遥感中的应用。E-mail: 835321654@qq.com

通讯作者:
项杰，E-mail: xjieah@aliyun.com

中图分类号: P412
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- 被引次数: 0
出版历程
- 收稿日期: 2021-06-08
- 录用日期: 2022-03-21
- 网络出版日期: 2022-03-12
- 刊出日期: 2023-05-15

Identification of the Reflected Signal in the COSMIC-2 Occultation Signal Using the Improved GoogLeNet Deep Learning Model

College of Meteorology and oceanography, National University of Defense Technology, Changsha 410008

Funds: National Natural Science Foundation of China (Grant 41475021)

摘要

摘要: GPS（Global Positioning System）掩星探测技术作为一种先进的大气探测手段，已广泛用于数值天气预报、气候和空间天气研究。掩星探测存在的问题之一是容易受到地球表面反射信号的干扰，识别和分离掩星探测信号中的反射信号有助于将掩星数据同化到数值天气预报系统中去，具有重要意义。本文提出一种基于改进的GoogLeNet深度学习模型（Im-GNet），并应用于COSMIC-2掩星探测数据来识别反射信号。本文选择了2020年1月1～9日的COSMIC-2掩星数据（conPhs文件），进行质量控制后，利用无线电全息方法得到掩星信号的无线电全息功率谱密度图像，然后训练得到Im-GNet深度学习模型，Im-GNet模型测试的准确率达到了96.4%，显著高于支持向量机（SVM）方法的结果。本文还分析了反射信号对掩星数据的影响，掩星事件的地理分布以及掩星反演数据（atmPrf文件）与NCEP再分析资料的12 h预报值（avnPrf文件）的折射率比较表明：有反射信号的掩星事件数据质量更好，所包含的大气信息更丰富。
- COSMIC-2掩星 /
- 深度学习 /
- 无线电全息技术 /
- GoogLeNet模型
Abstract: As an advanced atmospheric detection method, GPS (Global Positioning System) occultation detection technology has been widely used in numerical weather forecasting, climate, and space weather research. One of the problems in occultation detection is that it is easily interfered with by the reflected signals on the surface of the earth. Identifying and separating the reflected signals in the occultation detection signal helps assimilate the occultation data into the numerical weather prediction system, which has considerable importance. This study proposes a deep learning model based on improved GoogLenet (Im-GNet) model and applies it to COSMIC-2 occultation detection data to identify reflected signals. This study selects the COSMIC-2 occultation data (conPhs file) from 1 January to 9 January 2020. After quality control, the radio holography method is used to obtain the spatial spectrum image of the occultation signal, and the Im-GNet deep learning model is trained. The accuracy rate of the Im-GNet model test reached 96.4%, which is significantly higher than the result of the support vector machine method. This study also analyzes the impact of reflected signals on occultation data. The geographic distribution of occultation events and the refractivity comparison between the occultation inversion data (atmPrf file) and the NCEP (National Centers for Environmental Prediction) 12-h forecast files (avnPrf file) shows that the quality of the occultation event data with reflection signals is better, and the atmospheric information contained is richer.
- COSMIC-2 occultation /
- Deep learning /
- Radio holographic technology /
- GoogLeNet model

HTML全文

图 1 GPS掩星探测示意图

Figure 1. Schematic diagram of GPS occultation detection

下载: 全尺寸图片幻灯片

图 2 一次COSMIC-2掩星事件[（1.75°N，54.02°E），掩星编号为C2E1.2020.001.03.50.G19]的（a）L1波段（1.57542 GHz）信号、（b）L2波段（1.22760 GHz）信号的时间—频率域空间谱，（c）L1波段信号、（d）L2波段信号的影响高度—弯角域空间谱

Figure 2. Spatial spectrums of time–frequency domain for the (a) L1 wave band (1.57542 GHz) and (b) L2 wave band (1.22760 GHz) signals, spatial spectrums of height–bending angle domain for the (c) L1 wave band and (d) L2 wave band signals in a COSMIC-2 occultation event [(1.75°N, 54.02°E), occultation ID is C2E1.2020.001.03.50.G19]

下载: 全尺寸图片幻灯片

图 3 COSMIC-2掩星L1波段信号局地空间谱：（a）清晰的反射信号；（b）模糊的反射信号；（c）无反射信号

Figure 3. Local spatial spectrum of L1 wave band signal for COSMIC-2 occultation: (a) Clear reflection signal; (b) unclear reflection signal; (c) no-reflection signal

下载: 全尺寸图片幻灯片

图 4 Im-GNet（改进的GoogLeNet模型）网络结构

Figure 4. Im-GNet (improved GoogLenet model) network structure

下载: 全尺寸图片幻灯片

图 5 Im-GNet模型训练过程准确率和损失率的变化曲线

Figure 5. The change curves of accuracy and loss rate for training process of the Im-GNet model

下载: 全尺寸图片幻灯片

图 6 2020年1月1～9日的13307次COSMIC-2掩星事件的分布。蓝（红）色点代表有（无）反射信号

Figure 6. Distribution of 13307 COSMIC-2 occultation events from 1 January to 9 January 2020. Blue (red) dots represent clear reflection (no-reflection) signals

下载: 全尺寸图片幻灯片

图 7 COSMIC-2掩星事件与NCEP再分析资料的折射率廓线的误差均值和标准差：（a）有清晰反射信号；（b）有模糊反射信号；（c）没有反射信号。黑色曲线代表误差均值，黑色水平线段代表标准差，蓝色曲线代表掩星事件的数量

Figure 7. Mean error and standard deviation of the refractive index profile between COSMIC-2 occultation events and NCEP reanalysis data: (a) Clear reflection signals; (b) unclear reflection signals; (c) no-reflection signals. The black curve represents the mean error, the black horizontal line represents the standard deviation, and the blue curve represents the number of occultation events

下载: 全尺寸图片幻灯片

表 1 试验计算机的软、硬件配置

Table 1. Configuration for hardware and software

项目	配置
硬件	内存（RAM）：16 G；处理器：Inter(R) Core(TM)i7-9850H CPU
操作系统	Windows 10专业版
计算机语言	Python 3.7.0
机器学习框架/工具	GoogLeNet：Tensorflow 2.1.0 SVM：Scikit-learn 0.18.1

下载: 导出CSV

表 2 三种模型的测试结果

Table 2. Test results for the three models

模型	模块/核函数	准确率	运算时间/s
Im-GNet	Inception（3a、3b、4a）	96.4%	12423
Li-SVM	线性核函数	80.9%	2053
Ga-SVM	高斯核函数	73.8%	2804

下载: 导出CSV

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