Recognition-Aware HRRP Generation With Generative Adversarial Network

Huang, Yue; Wen, Yi; Shi, Liangchao; Ding, Xinghao

doi:10.1109/lgrs.2021.3056192

Cited by 14 publications

(3 citation statements)

References 17 publications

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“…It provides the distribution of target scattering points along the range direction and contains abundant information about the target structure, shape, and size 1 . HRRP is an essential structural feature of the target and, compared to Synthetic Aperture Radar (SAR) and Inverse Synthetic Aperture Radar (ISAR) images, it is easier to process and acquire 2 . As a result, HRRP target recognition has garnered significant attention within the Radar Automatic Target Recognition (RATR) community [3][4][5][6] .…”

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)

View full text Add to dashboard Cite

show abstract

“…The original data can be expanded by data augmentation methods to increase the number of samples and improve the data diversity. In recent years, it has been widely used in small sample recognition of radar targets [8][9]. Traditional data generation methods mainly utilize various transformations of existing data (such as rotation, flipping, scaling and translation, etc.)…”

Section: Introductionmentioning

confidence: 99%

Small Sample Target Recognition Based on Radar HRRP and SDAE-WACGAN

Yin,

Sheng,

Jiang

2024

IEEE Access

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The high resolution range profile (HRRP) of radar targets is commonly used for target recognition, and the recognition of non-cooperative targets is one of the urgent problems to be solved in radar target recognition. In order to address the noise impact and small sample issues in non-cooperative target recognition, this paper proposes a radar target HRRP recognition method based on SDAE-WACGAN. This method combines Stacked Denoising Auto-encoders (SDAE) and Weighted Auxiliary Classifier Generative Adversarial Networks (WACGAN). In this networks, the decoder of SDAE is used as the generator of WACGAN and the weight coefficients is introduced on the basis of ACGAN, so that the network can generate high-quality data that is more consistent with the real sample distribution, and can be more robust to noise. Experimental results show that compared with other commonly used models, the proposed method achieves higher recognition accuracy in scenarios with small samples and high noise, and demonstrates certain advantages in different SNRs and different number of sample sets.INDEX TERMS High resolution range profile (HRRP), non-cooperative target recognition, stacked denoising auto-encoders (SDAE), weighted auxiliary classifier generative adversarial networks (WACGAN).

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“…Traditional recognition networks based on electromagnetic datasets fail to cover all micro-motion parameters and have single target size parameters, leading to clear limitations and deficiencies when recognizing space target radar images which are heavily influenced by micro-motion and geometric parameters. Generative adversarial networks (GAN) have recently been introduced for synthesizing credible radar images [17][18][19][20]. Ibrahim Alnujaim et al proposed using GAN to increase the human micro-Doppler signatures training dataset [19].…”

Section: Introduction 1background and Motivationmentioning

confidence: 99%

Space Targets with Micro-Motion Classification Using Complex-Valued GAN and Kinematically Sifted Methods

Han,

Feng,

2023

Remote Sensing

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Space target classification based on micro-motion characteristics has become a subject of great interest in the field of radar, particularly when using deep learning techniques. However, in practical applications, the ability of deep learning is hampered by the available radar datasets. As a result, obtaining a sufficient amount of the training dataset is a daunting challenge. To address this issue, this paper presents a novel framework for space target classification, consisting of three distinct modules: dataset generation, the kinematically sifted module, and classification. Initially, the micro-motion model of cone-shaped space targets is constructed to analyze target characteristics. Subsequently, the dataset generation module employs a complex-valued generative adversarial network (CV-GAN) to generate a large number of time-range maps. These maps serve as the foundation for training the subsequent modules. Next, the kinematically sifted module is introduced to eliminate images that do not align with the micro-motion characteristics of space targets. By filtering out incompatible images, the module ensures that only relevant and accurate dataset is utilized for further analysis. Finally, the classification model is constructed using complex-valued parallel blocks (CV-PB) to extract valuable information from the target. Experimental results validate the effectiveness of the proposed framework in space micro-motion target classification. The main contribution of the framework is to generate a sufficient amount of high-quality training data that conforms to motion characteristics, and to achieve accurate classification of space targets based on their micro-motion signatures. This breakthrough has significant implications for various applications in space target classification.

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Recognition-Aware HRRP Generation With Generative Adversarial Network

Cited by 14 publications

References 17 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

Small Sample Target Recognition Based on Radar HRRP and SDAE-WACGAN

Space Targets with Micro-Motion Classification Using Complex-Valued GAN and Kinematically Sifted Methods

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