Machine learning for deep space network antenna motions detection

Victor Li; James McKelvy; Lin Yi

doi:10.1117/12.2608321

2 March 2022 Machine learning for deep space network antenna motions detection

Victor Li, James McKelvy, Lin Yi

Author Affiliations +

Proceedings Volume 12019, AI and Optical Data Sciences III; 120190H (2022) https://doi.org/10.1117/12.2608321
Event: SPIE OPTO, 2022, San Francisco, California, United States

Abstract

Highly stable frequency and timing standards are essential for deep-space missions and radio science. At the NASA Deep Space Network (DSN), these standards are distributed through a network of underground fiber cables to support several Goldstone antennas. Independently developed frequency-measuring instruments generate tremendous quantities of data to monitor and validate the antennas’ stringent frequency requirements. In this paper, we propose a lightweight processing tool capable of detecting disturbances on the frequency signal caused by DSN antenna motions. Our training data is sampled from the movement log of the antenna of interest and the generated data from the fiber optic metrology instrument linked to the antenna. We demonstrate that a convolutional neural network (CNN) model can achieve high accuracies on classifying instances of antenna movements and is an effective predictor when used iteratively on longer, variable stretches of metrology data. The simplicity, low training cost, and high accuracies of our model strongly suggest its efficacy in identifying and troubleshooting frequency disturbances caused by the antenna.

Conference Presentation

Citation Download Citation

Victor Li, James McKelvy, and Lin Yi "Machine learning for deep space network antenna motions detection", Proc. SPIE 12019, AI and Optical Data Sciences III, 120190H (2 March 2022); https://doi.org/10.1117/12.2608321

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