Meteor Head Echo Detection at Multiple High-Power Radar Facilities via a Convolutional Neural Network Trained on Synthetic Data

High-power large-aperture (HPLA) radar instruments are capable of detecting thousands of meteors in timespans of hours, and their radar echoes provide insights into properties of the lower thermosphere. Head echoes, which occur due to radio wave scattering from plasma surrounding the ablating meteoroid, are so abundant in HPLA data that manual identification using traditional detection methods is prohibitively time-consuming. Previous work has demonstrated that convolutional neural networks (CNNs) accurately detect head echoes. However, training a CNN requires thousands of head echo examples observed at the same facility and with the same pulse code. Since such pre-existing data is often unavailable, a synthetic model is developed to generate simulated radar head echo observations with any given carrier frequency and pulse code. Real instances of radar clutter, such as long-duration meteor trails and range-aliased spread F events, are additively combined with a fraction of the synthetic head echo examples, which allows the CNN to differentiate between head echoes and clutter. The CNNs are trained using synthetic datasets with 50,000 simulated head echo examples. Three CNNs are trained for each of three radar experiments at Resolute Bay Incoherent Scatter Radar (RISR-N), MIT Haystack Observatory (MHO), and Jicamarca Radio Observatory (JRO), where concurrent meteor observations were performed for eight hours in October 2019. Each CNN is tested on a subset of actual data containing hundreds of head echoes. The CNNs prove accurate for detecting meteors, with greater than 99% overall accuracy at JRO, where equatorial effects that create radar clutter are most prominent. Furthermore, most incorrect detections are false positives rather than false negatives, indicating that a CNN is useful for identifying a near-exhaustive set of meteors in an HPLA dataset after eliminating false positives. Statistics for the meteor populations at each facility are presented.