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Performance Comparison of Moving Target Classification based on Deep Learning | IEEE Conference Publication | IEEE Xplore

Performance Comparison of Moving Target Classification based on Deep Learning


Abstract:

Radar target detection is a basic but important process of radar systems, and it is difficult to distinguish and measure targets in real-world environments. Therefore, di...Show More

Abstract:

Radar target detection is a basic but important process of radar systems, and it is difficult to distinguish and measure targets in real-world environments. Therefore, distinguishing between humans and animals based on radar signals is a difficult task in the field of ground radar. The radar signal processing section uses the in-phase/quadrature- phase (I/Q) matrix radar signal data and geolocation types as inputs and performs binary classification to classify animals and humans. In this radar signal processing, deep learning methods are adopted as feasible solutions. However, there is a limited lack of training data in the real world and a problem with jamming signals, which are adversarial attacks. However, it is difficult to collect a lot of training data in a real-time environment. Reflecting this, we learn only some data from MAFAT Radar Challenge data to compare and analyze the classification performance of conventional methods convolutional neural network (CNN) and CNN-based U-Net and U-Net with residual blocks U-Net (Res- UNet) algorithms.
Date of Conference: 19-21 October 2022
Date Added to IEEE Xplore: 25 November 2022
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Conference Location: Jeju Island, Korea, Republic of

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