FURI | Spring 2026

Effect of Ground Reflectance on Shadow-Based NLOS Drone Tracking Using Synthetic Outdoor Rendering

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This research investigates how outdoor materials influence shadow-based drone tracking in non-line-of-sight (NLOS) environments. A synthetic dataset is generated using Blender to model surfaces such as grass, sand, and gravel under varying solar conditions, providing labeled data for analyzing shadow behavior and improving tracking performance. This approach supports counter-unmanned aerial vehicle (counter-UAV) detection by enabling more reliable localization in complex outdoor settings. Future work will apply machine learning models to the dataset to further enhance NLOS tracking accuracy.