Thesis Defense Announcement for Kester Nucum – 2/18/25 at 1:30 pm
January 28, 2025
Thesis Title: Machine learning classification of drones and birds with radar micro-Doppler, camera imagery, and radar-camera fusion
When: 2/18/25 at 1:30 pm
Where: Simrall 228 (in-person) and Microsoft Teams (virtual) – Meeting Link – Meeting ID: 274 173 693 158 – Passcode: Us7gQ9XP
Candidate: Kester Nucum
Degree: Master of Science in Electrical and Computer Engineering
Committee Members: Dr. John Ball, Dr. Samee Khan, Dr. Ali Gurbuz
Abstract:
Counter-drone systems employ sensors such as radars and cameras to detect, track, and identify drones. These systems can confuse birds with drones, and the advent of bird-like drones exacerbates this problem. This thesis explores radar micro-Doppler, camera imagery, and their fusion for drone and bird classification. First, micro-Doppler models of quadcopters, birds, and bird-like drones were developed to generate a dataset of synthetic spectrograms for training support vector machine, k-nearest neighbors, Naïve Bayes, and convolutional neural network (CNN) classifiers. Second, transfer learning was applied on YOLOv10, a recently developed You Only Look Once object detector model, to detect and classify drones and birds in visual images. Lastly, a radar-camera fusion system that fuses a radar detector and CNN classifier and a camera YOLOv10 detector/classifier at the decision-level is proposed. A dataset of real-world spectrograms and images of quadcopters, bird-like drones, and birds in flight was collected to test this system.