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Media type:
E-Article
Title:
End-to-End Radar HRRP Target Recognition Based on Integrated Denoising and Recognition Network
Contributor:
Liu, Xiaodan;
Wang, Li;
Bai, Xueru
Published:
MDPI AG, 2022
Published in:
Remote Sensing, 14 (2022) 20, Seite 5254
Language:
English
DOI:
10.3390/rs14205254
ISSN:
2072-4292
Origination:
Footnote:
Description:
For high-resolution range profile (HRRP) radar target recognition in a low signal-to-noise ratio (SNR) scenario, traditional methods frequently perform denoising and recognition separately. In addition, they assume equivalent contributions of the target and the noise regions during feature extraction and fail to capture the global dependency. To tackle these issues, an integrated denoising and recognition network, namely, IDR-Net, is proposed. The IDR-Net achieves denoising through the denoising module after adversarial training, and learns the global relationship of the generated HRRP sequence using the attention-augmented temporal encoder. Furthermore, a hybrid loss is proposed to integrate the denoising module and the recognition module, which enables end-to-end training, reduces the information loss during denoising, and boosts the recognition performance. The experimental results on the measured HRRPs of three types of aircraft demonstrate that IDR-Net obtains higher recognition accuracy and more robustness to noise than traditional methods.