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A small 30-MHz coherent scatter radar imager has been deployed to the HAARP facility near Gakona, Alaska, for studying the effects of ionospheric modifications on polar mesospheric summer echoes (PMSEs). In initial tests scheduled during the 2025 Perseids shower, the radar observed a remarkably long-lived non-specular meteor trail. Although not optimized for the purpose, the radar mode used was sufficiently general to permit analysis of the echoes. This paper explores the results of the analysis and highlights the adjacent science that can be performed in the backdrop of multi-purpose geospace research facilities like HAARP. Lessons learned from the exercise and perspectives on the role and value of the geospace facilities are discussed.
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A Machine Learning Algorithm to Detect and Analyze Meteor Echoes Observed by the Jicamarca Radar
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.
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- PAR ID:
- 10486968
- Publisher / Repository:
- MDPI
- Date Published:
- Journal Name:
- Remote Sensing
- Volume:
- 15
- Issue:
- 16
- ISSN:
- 2072-4292
- Page Range / eLocation ID:
- 4051
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.3390/rs15164051
