Software, Trained Models and Test Data for Convolutional Neural Network Trained to Detect Head Echoes in HPLA Radar Data
- This archive contains the test data, training weights, and Python code for the convolutional neural network (CNN) used to generate results in the paper "Meteor Head Echo Detection at Multiple High-Power Radar Facilities via a Convolutional Neural Network Trained on Synthetic Data" authored by Trevor Hedges, Nicolas Lee, and Sigrid Elschot. The algorithm described in the paper uses synthetic examples of meteor head echoes to train the neural networks, and real examples of head echoes from multiple radar facilities to test the neural networks. Synthetic data can be produced for any radar facility capable of observing meteor head echoes. In this work, the method is tested on three radar facilities, including the Resolute Bay Incoherent Scatter Radar North (RISR-N), Millstone Hill Observatory (MHO), and Jicamarca Radio Observatory (JRO). Details on how to reproduce the results of the paper are specified in the readme file.
|Type of resource
|mixed material, text, Dataset, software, multimedia
|January 24, 2024
|October 17, 2023; October 17, 2023
- Use and reproduction
- User agrees that, where applicable, content will not be used to identify or to otherwise infringe the privacy or confidentiality rights of individuals. Content distributed via the Stanford Digital Repository may be subject to additional license and use restrictions applied by the depositor.
- This work is licensed under a Creative Commons Attribution 4.0 International license (CC BY).
- Preferred citation
- Hedges, T., Lee, N., and Elschot, S. (2023). Software, Trained Models and Test Data for Convolutional Neural Network Trained to Detect Head Echoes in HPLA Radar Data. Stanford Digital Repository. Available at https://purl.stanford.edu/zn070dk3564. https://doi.org/10.25740/zn070dk3564.
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