Publications
Detailed Information
Time-Domain Measurement Data Accumulation for Slow Moving Point Target Detection in Heavily Cluttered Environments Using CNN
Cited 0 time in
Web of Science
Cited 0 time in Scopus
- Authors
- Issue Date
- 2023-11
- Publisher
- 한국전자파학회
- Citation
- Journal of Electromagnetic Engineering and Science, Vol.23 No.6, pp.490-501
- Abstract
- In modern radars, the target detection probability is increased by lowering the detection threshold via signal processing to detect a point target with a small radar cross-section value. However, a lower threshold increases the number of false targets. In the conventional tracking method, which uses a general tracking filter, the measurement data between scans should be compared. Therefore, for a large amount of acquired measurement data, the computational complexity can be reduced by accumulating the acquired measurement data over time, recognizing the target movement as a pattern, and training a convolutional neural network (CNN) model. Here, we propose a method to create a desired target scenario by transfer learning and estimate the target position using the activation map of a binary detector CNN model. The model can detect a target using the actual acquired radar data, and the processing time remains constant, regardless of the number of false alarms.
- ISSN
- 2234-8409
- Files in This Item:
- There are no files associated with this item.
Related Researcher
- Graduate School of Convergence Science & Technology
- Department of Intelligence and Information
Item View & Download Count
Items in S-Space are protected by copyright, with all rights reserved, unless otherwise indicated.