Classification and Discrimination of Birds and Small Drones Using Radar Micro-Doppler Spectrogram Images
Abstract
This paper investigates the use of micro-Doppler spectrogram signatures of flying targets, such as drones and birds, to aid in their remote classification. Using a custom-designed 10-GHz continuous wave (CW) radar system, measurements from different scenarios on a variety of targets were recorded to create datasets for image classification. Time/velocity spectrograms generated for micro-Doppler analysis of multiple drones and birds were used for target identification and movement classification using TensorFlow. Using support vector machines (SVMs), the results showed an accuracy of about 90% for drone size classification, about 96% for drone vs. bird classification, and about 85% for individual drone and bird distinction between five classes. Different characteristics of target detection were explored, including the landscape and behavior of the target.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- May 18, 2023
- Source ID
- 10.3390/signals4020018
Entities
People
- Bryan Tsang
- Ram M Narayanan
- Ramesh Bharadwaj
Organizations
- Office of Naval Research
- Pennsylvania State University
- United States Naval Research Laboratory