TY - JOUR
T1 - Meteor Detection With a New Computer Vision Approach
AU - Li, Yanlin
AU - Galindo, Freddy
AU - Urbina, Julio
AU - Zhou, Qihou
AU - Huang, Tai Yin
N1 - Publisher Copyright:
© 2022. American Geophysical Union. All Rights Reserved.
PY - 2022/10
Y1 - 2022/10
N2 - A novel computer vision-based meteor head echo detection algorithm is developed to study meteor fluxes and their physical properties, including initial range, range coverage, and radial velocity. The proposed Algorithm for Head Echo Automatic Detection (AHEAD) comprises a feature extraction function and a Convolutional Neural Network (CNN). The former is tailored to identify meteor head echoes, and then a CNN is employed to remove false alarms. In the testing of meteor data collected with the Jicamarca 50 MHz incoherent scatter radar, the new algorithm detects over 180 meteors per minute at dawn, which is 2 to 10 times more sensitive than prior manual or algorithmic approaches, with a false alarm rate less than 1 percent. The present work lays the foundation of developing a fully automatic AI-meteor package that detects, analyzes, and distinguishes among many types of meteor echoes. Furthermore, although initially evaluated for meteor data collected with the Jicamarca VHF incoherent radar, the new algorithm is generic enough that can be applied to other facilities with minor modifications. The CNN removes up to 98 percent of false alarms according to the testing set. We also present and discuss the physical characteristics of meteors detected with AHEAD, including flux rate, initial range, line of sight velocity, Signal-to-Noise Ratio, and noise characteristics. Our results indicate that stronger meteor echoes are detected at a slightly lower altitude and lower radial velocity than other meteors.
AB - A novel computer vision-based meteor head echo detection algorithm is developed to study meteor fluxes and their physical properties, including initial range, range coverage, and radial velocity. The proposed Algorithm for Head Echo Automatic Detection (AHEAD) comprises a feature extraction function and a Convolutional Neural Network (CNN). The former is tailored to identify meteor head echoes, and then a CNN is employed to remove false alarms. In the testing of meteor data collected with the Jicamarca 50 MHz incoherent scatter radar, the new algorithm detects over 180 meteors per minute at dawn, which is 2 to 10 times more sensitive than prior manual or algorithmic approaches, with a false alarm rate less than 1 percent. The present work lays the foundation of developing a fully automatic AI-meteor package that detects, analyzes, and distinguishes among many types of meteor echoes. Furthermore, although initially evaluated for meteor data collected with the Jicamarca VHF incoherent radar, the new algorithm is generic enough that can be applied to other facilities with minor modifications. The CNN removes up to 98 percent of false alarms according to the testing set. We also present and discuss the physical characteristics of meteors detected with AHEAD, including flux rate, initial range, line of sight velocity, Signal-to-Noise Ratio, and noise characteristics. Our results indicate that stronger meteor echoes are detected at a slightly lower altitude and lower radial velocity than other meteors.
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U2 - 10.1029/2022RS007515
DO - 10.1029/2022RS007515
M3 - Article
AN - SCOPUS:85141656909
SN - 0048-6604
VL - 57
JO - Radio Science
JF - Radio Science
IS - 10
M1 - e2022RS007515
ER -