Transformer-based segmentation network for false target suppression in radar detection

Yu Ye; Yuhao Yang; Junpeng Yu

doi:10.1117/12.3033816

13 June 2024 Transformer-based segmentation network for false target suppression in radar detection

Yu Ye, Yuhao Yang, Junpeng Yu

Proceedings Volume 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024); 1318069 (2024) https://doi.org/10.1117/12.3033816
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 2024, Guangzhou, China

Abstract

During the process of radar detection, various factors such as noise and clutter interfere with the received echo signals, leading to the detection of a large number of false targets. This poses significant challenges to the detection, tracking, and identification of real targets. This paper introduces a Transformer-based segmentation network for false target suppression. It innovatively transforms the problem of false target suppression in sequential signals into an image segmentation task. The dice loss is effectively employed to address the issue of class imbalance between targets and backgrounds. Furthermore, the introduction of the transformer module further enhances the segmentation performance of the model. The proposed method is validated on real radar measurement data, demonstrating both the effectiveness of the designed model and the improvements brought by each module.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yu Ye, Yuhao Yang, and Junpeng Yu "Transformer-based segmentation network for false target suppression in radar detection", Proc. SPIE 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 1318069 (13 June 2024); https://doi.org/10.1117/12.3033816

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