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- W4387325244 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." @default.
- W4387325244 created "2023-10-04" @default.
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- W4387325244 date "2023-10-04" @default.
- W4387325244 modified "2023-10-18" @default.
- W4387325244 title "Preliminary analysis of drone propeller signals using wingbeat-modulation lidar" @default.
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- W4387325244 doi "https://doi.org/10.1117/12.2676175" @default.
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