| Autor: |
Ram M. Narayanan, Bryan Tsang, Ramesh Bharadwaj |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
Signals, Vol 4, Iss 2, Pp 337-358 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2624-6120 |
| DOI: |
10.3390/signals4020018 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
