A Machine Learning Algorithm to Detect and Analyze Meteor Echoes Observed by the Jicamarca Radar
A Machine Learning Algorithm to Detect and Analyze Meteor Echoes Observed by the Jicamarca Radar
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DOI：
10.3390/rs15164051
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发表时间：
2023-08
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影响因子：
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通讯作者：
Yanlin Li;F. Galindo;J. Urbina;Qihou Zhou;Tai-Yin Huang
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作者：
Yanlin Li;F. Galindo;J. Urbina;Qihou Zhou;Tai-Yin Huang
我们提出了一种机器学习方法来检测和分析流星回波（MADAME），这是一种雷达数据处理工作流程，采用监督和无监督学习的先进机器学习技术。我们的研究结果表明，YOLOv4是一种基于卷积神经网络（CNN）的一级目标检测模型，在检测和识别处理后的雷达信号中的流星头部和尾部回波方面表现非常出色。该探测器每分钟可以在Jicamarca高功率大孔径（HPLA）雷达获得的测试数据中识别80多个回波。MADAME还能够以干涉仪模式自主处理数据，以及确定目标的辐射源和矢量速度。在测试数据中，Eta宝瓶座流星雨可以从MADAME自动分析的流星辐射源分布中清楚地识别出来，从而证明了所提出的算法的功能。此外，MADAME发现，大约50%的流星是在倾斜和近倾斜的圆形轨道上运行的。此外，流星头回波与尾迹更有可能起源于流星雨源。我们的研究结果突出了先进的机器学习技术在雷达信号处理中的能力，为促进未来和新的流星研究提供了一个有效而强大的工具。
We present a machine-learning approach to detect and analyze meteor echoes (MADAME), which is a radar data processing workflow featuring advanced machine-learning techniques using both supervised and unsupervised learning. Our results demonstrate that YOLOv4, a convolutional neural network (CNN)-based one-stage object detection model, performs remarkably well in detecting and identifying meteor head and trail echoes within processed radar signals. The detector can identify more than 80 echoes per minute in the testing data obtained from the Jicamarca high power large aperture (HPLA) radar. MADAME is also capable of autonomously processing data in an interferometer mode, as well as determining the target’s radiant source and vector velocity. In the testing data, the Eta Aquarids meteor shower could be clearly identified from the meteor radiant source distribution analyzed automatically by MADAME, thereby demonstrating the proposed algorithm’s functionality. In addition, MADAME found that about 50 percent of the meteors were traveling in inclined and near-inclined circular orbits. Furthermore, meteor head echoes with a trail are more likely to originate from shower meteor sources. Our results highlight the capability of advanced machine-learning techniques in radar signal processing, providing an efficient and powerful tool to facilitate future and new meteor research.