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Time-Domain Measurement Data Accumulation for Slow Moving Point Target Detection in Heavily Cluttered Environments Using CNN

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Authors

Cho, Wonmin; Kwak, Nojun

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
URI
https://hdl.handle.net/10371/198963
DOI
https://doi.org/10.26866/jees.2023.6.r.194
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  • Graduate School of Convergence Science & Technology
  • Department of Intelligence and Information
Research Area Feature Selection and Extraction, Object Detection, Object Recognition

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