One-Shot HRRP Generation for Radar Target Recognition

Shi, Liangchao; Liang, Zhehan; Wen, Yi; Zhuang, Yihong; Huang, Yue; Ding, Xinghao

doi:10.1109/lgrs.2021.3063241

Cited by 11 publications

(4 citation statements)

References 27 publications

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“…Deep learning has experienced rapid development in recent years due to its advantages in automatic feature extraction and robust classification and regression capabilities [10][11][12] . An increasing number of researchers are paying attention to this data-driven learning approach, expecting to enhance the recognition performance of radar HRRP targets [13][14][15][16][17][18][19] . A study on HRRP recognition using a one-dimensional Convolutional Neural Network (CNN) was conducted 16 .…”

Section: Introductionmentioning

confidence: 99%

Ship HRRP target recognition method based on transformer and convolutional attention network

ZIJIE,

GUANGFEN,

et al. 2024

Fifteenth International Conference on Signal Processing Systems (ICSPS 2023)

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The radar's high-resolution range profile (HRRP) data structure is complex, and extracting stable and reliable features from it is crucial for HRRP target recognition. In this paper, we propose to use the convolution module to extract local spatial features of HRRP and use the positional encoding to embed the position information to generate new temporal features, and then capture the long-term dependency within the distance unit of HRRP through the multi-head new selfattention mechanism of the Transformer encoder, to construct a reliable feature extraction method for the HRRP target. Finally, a new deep learning model CNN-TEAN (CNN TransEncoder-Attention Network), based on one-dimensional residual convolution, Transformer encoder, and attention mechanism, is formed using the attention mechanism, fully connected layer, and softmax for classification. Using six simulated ship target data types for experimental validation, the CNN-TEAN model proposed in this paper can achieve a higher recognition rate than RNN, LSTM, and SVM models.

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Section: Introductionmentioning

confidence: 99%

Ship HRRP target recognition method based on transformer and convolutional attention network

ZIJIE,

GUANGFEN,

et al. 2024

Fifteenth International Conference on Signal Processing Systems (ICSPS 2023)

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“…Recently, the generative adversarial network (GAN) has been introduced as a novel way to train a generative model, which could learn the complex distributions through the adversarial training between the generator and the discriminator [38]. Currently, GAN has been successfully applied to data generation [39,40], image conversion and classification [41,42], speech enhancement [43] and so on, which provides an effective way to blind HRRP denoising.…”

Section: Introductionmentioning

confidence: 99%

End-to-End Radar HRRP Target Recognition Based on Integrated Denoising and Recognition Network

2022

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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.

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“…Recently proposed models also show that the class (or label) information is helpful to improve the quality of generation [43,44]. There are few works on HRRP generation as well [45,46]. In [45], DCGAN is used for imbalanced HRRP recognition and the quality is comprehensively evaluated on data-, feature-and recognition-level.…”

Section: Introductionmentioning

confidence: 99%

“…In [45], DCGAN is used for imbalanced HRRP recognition and the quality is comprehensively evaluated on data-, feature-and recognition-level. Shi proposed to use a pre-trained GAN and transferred features for generation from only one known sample [46]. Although the existing approaches have demonstrated promising results in radar data generation, the aspect information is not integrated into the generation model and the generation quality of the unacquired aspects still needs improvements.…”

Section: Introductionmentioning

confidence: 99%

Multi-View HRRP Generation With Aspect-Directed Attention GAN

Song

Zhou

Yang

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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In radar automatic target recognition (RATR), highresolution range profile (HRRP) has received intensive attention due to its low computational cost. As HRRP is sensitive to the aspect of the target, a training set covering sufficient aspects is essential to the success of a RATR model, which is however intractable in complex environment with noncooperative targets. In this paper, an aspect-directed attention generative adversarial network (ADA-GAN) is proposed to generate multi-view HRRPs using real samples from few aspects. The key is that the HRRPs from the similar targets share the same aspect variation pattern. Hence, an HRRP is decomposed into its identity and aspect features via an aspect-directed disentangled representation network with self-attention modules. In the training stage, the decomposition network and the aspect variation pattern are learned from full aspect samples of cooperative targets. When generation, the desired multi-view HRRPs of the noncooperative target are synthesized by its identity features extracted from few aspect samples and the learned aspect variation pattern. Three types of experiments on the simulated and measured datasets demonstrate the generation performances of our method. First, the generated HRRPs are visually compared with the truth. Second, the similarity of the scattering center power and handcrafted feature distributions are quantitatively evaluated. Finally, recognition experiments verify the feasibility of data augmentation with the generated HRRPs. Extensive results show the superior performance of our method over other state-of-theart methods.

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One-Shot HRRP Generation for Radar Target Recognition

Cited by 11 publications

References 27 publications

Ship HRRP target recognition method based on transformer and convolutional attention network

Ship HRRP target recognition method based on transformer and convolutional attention network

End-to-End Radar HRRP Target Recognition Based on Integrated Denoising and Recognition Network

Multi-View HRRP Generation With Aspect-Directed Attention GAN

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