2022
DOI: 10.3390/rs14205254
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End-to-End Radar HRRP Target Recognition Based on Integrated Denoising and Recognition Network

Abstract: 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 a… Show more

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Cited by 11 publications
(3 citation statements)
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“…Radar Automatic Target Recognition (RATR) came into being to satisfy the growing demands of military surveillance, civil monitoring and environmental assessments. RATR is usually implemented based on high-resolution range profiles (HRRP) [6], synthetic aperture radar images [7][8][9], and inverse synthetic aperture radar images [10]. HRRP is more accessible and reflects physical structure information, such as the size of the target and the distribution of scattering points, which receives extensive attention in RATR [6].…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…Radar Automatic Target Recognition (RATR) came into being to satisfy the growing demands of military surveillance, civil monitoring and environmental assessments. RATR is usually implemented based on high-resolution range profiles (HRRP) [6], synthetic aperture radar images [7][8][9], and inverse synthetic aperture radar images [10]. HRRP is more accessible and reflects physical structure information, such as the size of the target and the distribution of scattering points, which receives extensive attention in RATR [6].…”
Section: Introductionmentioning
confidence: 99%
“…RATR is usually implemented based on high-resolution range profiles (HRRP) [6], synthetic aperture radar images [7][8][9], and inverse synthetic aperture radar images [10]. HRRP is more accessible and reflects physical structure information, such as the size of the target and the distribution of scattering points, which receives extensive attention in RATR [6]. The construction of a database containing complete target classes is the basis for RATR.…”
Section: Introductionmentioning
confidence: 99%
“…Considering the temporal dependencies and multi-domain features within HRRPs, Zeng et al (2022a) proposed the Multi-Input Convolutional Gated Recurrent Unit (MIConvGRU) structure, which utilizes temporal, frequency, and time-domain information for recognition. Furthermore, there exist studies that leverage a combination of physical knowledge, attention mechanism, and deep networks ( Zhang L. et al, 2020 ; Pan et al, 2021 ; Liu et al, 2022 ). Zhang and Zhang (2022) , used self-attention to weight and interactively concatenate different polarization channels.…”
Section: Introductionmentioning
confidence: 99%