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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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Investigating Rotation and Anisotropic Ablation of Small Meteoroids and Their Effects on Head Echo Plasma Formation via Computational Techniques
Abstract High‐power large‐aperture radar instruments observe numerous meteor head echoes per minute. Head echoes result from reflections of radio waves from plasma surrounding meteoroids as they enter Earth's atmosphere. Knowledge of the spatial distribution of electrons in this plasma is essential to determining the mass loss rate of the meteor as a function of its measured radar cross‐section. Prior work applies theoretical and computational methods to determine the electron density distribution, but assumes the meteoroid emits neutral particles uniformly across its surface. In this paper, a numerical surface ablation model demonstrates that meteoroid mass loss may occur preferentially in the direction facing the oncoming atmosphere. Specifically, meteoroid mass loss becomes proportional to the frontal surface area facing the freestream atmosphere in the limit of high Biot number, but remains isotropic in the limit of low Biot number. Meteoroid rotation has a small effect on the direction of ejected mass, but the effect is insignificant compared to variation in meteoroid properties that affect the Biot number. This result informs our computational meteor plasma model, in which we compare the effect of meteoroid vaporization on the plasma distribution in the limits of low versus high Biot number. The resulting electron density profiles demonstrate order‐of‐magnitude agreement between each other, with peak difference of 70% immediately upstream of the meteoroid. This implies that the directional distribution of vaporizing neutrals likely does not significantly influence head echo observations, lending credence to existing work that assumes isotropic ablation.
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- Award ID(s):
- 2301645
- PAR ID:
- 10616238
- Publisher / Repository:
- JGR Space Physics
- Date Published:
- Journal Name:
- Journal of Geophysical Research: Space Physics
- Volume:
- 130
- Issue:
- 6
- ISSN:
- 2169-9380
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract High‐power large‐aperture radar instruments are capable of detecting thousands of meteor head echoes within hours of observation, and manually identifying every head echo is prohibitively time‐consuming. Previous work has demonstrated that convolutional neural networks (CNNs) accurately detect head echoes, but training a CNN requires thousands of head echo examples manually identified at the same facility and with similar experiment parameters. Since pre‐labeled data is often unavailable, a method is developed to simulate head echo observations at any given frequency and pulse code. Real instances of radar clutter, noise, or ionospheric phenomena such as the equatorial electrojet are additively combined with synthetic head echo examples. This enables the CNN to differentiate between head echoes and other phenomena. CNNs are trained using tens of thousands of simulated head echoes at each of three radar facilities, where concurrent meteor observations were performed in October 2019. Each CNN is tested on a subset of actual data containing hundreds of head echoes, and demonstrates greater than 97% classification accuracy at each facility. The CNNs are capable of identifying a comprehensive set of head echoes, with over 70% sensitivity at all three facilities, including when the equatorial electrojet is present. The CNN demonstrates greater sensitivity to head echoes with higher signal strength, but still detects more than half of head echoes with maximum signal strength below 20 dB that would likely be missed during manual detection. These results demonstrate the ability of the synthetic data approach to train a machine learning algorithm to detect head echoes.more » « less
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Abstract Meteoroids of sub‐milligram sizes burn up high in the Earth's atmosphere and cause streaks of plasma trails detectable by meteor radars. The altitude at which these trails, or meteors, form depends on a number of factors including atmospheric density and the astronomical source populations from which these meteoroids originate. A previous study has shown that the altitude of these meteors is affected by long‐term linear trends and the 11‐year solar cycle related to changes in our atmosphere. In this work, we examine how shorter diurnal and seasonal variations in the altitude distribution of meteors are dependent on the geographical location at which the measurements are performed. We use meteoroid altitude data from 18 independent meteor radar stations at a broad range of latitudes and investigate whether there are local time (LT) and seasonal variations in the altitude of the peak meteor height, defined as the majority detection altitude of all meteors within a certain period, which differ from those expected purely from the variation in the visibility of their astronomical source. We find a consistent LT and seasonal response for the Northern Hemisphere locations regardless of latitude. However, the Southern Hemisphere locations exhibit much greater LT and seasonal variation. In particular, we find a complex response in the four stations located within the Southern Andes region, which indicates that the strong dynamical atmospheric activity, such as the gravity waves prevalent here, disrupts, and masks the seasonality and dependence on the astronomical sources.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1029/2025JA033883
