Preliminary analysis of drone propeller signals using wingbeat-modulation lidar

John Fike; Trevor C. Vannoy; Nathaniel Sweeney; Joseph A. Shaw; Bradley M. Whitaker

doi:10.1117/12.2676175

4 October 2023 Preliminary analysis of drone propeller signals using wingbeat-modulation lidar

John Fike, Trevor C. Vannoy, Nathaniel Sweeney, Joseph A. Shaw, Bradley M. Whitaker

Proceedings Volume 12675, Applications of Machine Learning 2023; 126750R (2023) https://doi.org/10.1117/12.2676175
Event: SPIE Optical Engineering + Applications, 2023, San Diego, California, United States

Abstract

Unmanned aerial vehicles (UAVs) have enjoyed a meteoric rise in both capability and accessibility—a trend that shows no signs of slowing. This has led to a growing need for detect-and-avoid technologies. These increasingly commonplace events have resulted in the development of a number of UAV detection methods, most of which are based on either radar, acoustics, visual, passive radio-frequency, or lidar detection technology. With regards to software, many of these UAV detection systems have begun to implement machine learning (ML) as a means to improve detection and classification capabilities. In this work, we detail a new lidar and ML-based propeller rotation analysis and classification method using a wingbeat-modulation lidar system. This system has the potential to sense characteristics, such as propeller speed and pitch, that other systems struggle to detect. This paper is an exploration into the preliminary development of our method, and into its potential capabilities and limitations. Using this method, propeller speed could be detected with a worst-case percent error of approximately 3.7% and an average percent error of approximately 2% when the beam was positioned on the propeller. Furthermore, Wide Neural Networks were able to accurately detect and characterize propeller signals when trained to determine either beam position or propeller orientation.

Conference Presentation

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

John Fike, Trevor C. Vannoy, Nathaniel Sweeney, Joseph A. Shaw, and Bradley M. Whitaker "Preliminary analysis of drone propeller signals using wingbeat-modulation lidar", Proc. SPIE 12675, Applications of Machine Learning 2023, 126750R (4 October 2023); https://doi.org/10.1117/12.2676175

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