Radar Target Recognition using Salient Keypoint Descriptors and Multitask Sparse Representation

Karine, Ayoub; Toumi, Abdelmalek; Khenchaf, Ali

doi:10.20944/preprints201804.0251.v1

Cited by 16 publications

(3 citation statements)

References 8 publications

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“…Moreover, with the rapid development of modern radar technology, the rapid rise of wideband radar has greatly expanded the information acquisition ability of radar system to target and environment, making wideband radar become an important means of getting target characteristic data. These data, mainly including highresolution range profile (HRRP), ISAR image and synthetic aperture radar (SAR) image, are widely used in radar automatic target recognition (RATR) research [1], [2]. For the research of satellite target recognition, HRRP and ISAR image are the common target characteristic data: HRRP is the 1-D projection of target scattering centers along the radar line of sight (LOS), and ISAR image achieves 2-D highresolution imaging for moving targets by obtaining highresolution in both range and azimuth directions.…”

Section: A Problem Statementmentioning

confidence: 99%

“…If the shift is replaced by a more general transformation from group G , we could get the definition of group convolution. Notice that both the input image f and the filter  are functions of the plane 2 for the first layer of group CNN, but the feature mapping f  ★ is a function of the discrete group G . So the filter  must also be functions on G , and the correlation operation for all layers after the first is a little different for that of the first layer.…”

Section: B Equivariance Property Of Traditional Cnn and Group Convolutionmentioning

confidence: 99%

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A Deformation Robust ISAR Image Satellite Target Recognition Method Based on PT-CCNN

et al. 2021

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To tackle the inherent unknown deformation (e.g., translation, rotation and scaling) of the inverse synthetic aperture radar (ISAR) images, a deep polar transformer-circular convolutional neural network, i.e., PT-CCNN, is proposed to achieve deformation robust ISAR image automatic target recognition (ATR) in this paper. Inspired by human visual system and canonical coordinate of Lie-groups, we adopt a polar transformer module to transform the deformation ISAR images to the log-polar representations, before which a conventional convolutional neural network (CNN) is utilized to predict the origin of log-polar transformation. The above techniques make the proposed network invariant to translation, and equivariant to rotation and scaling. On this basis, for the log-polar representations with wrap-around structure, a circular convolutional neural network (CCNN) is further applied to extract more effective and highly discriminative features and improve recognition accuracy. The proposed network is end-to-end trainable with a classification loss, and could extract deformation-robust and essential features automatically. For multiple practical ISAR image datasets of six satellites, the performance testing and comparison experiments demonstrate that the techniques utilized in PT-CCNN are effective, and our proposed network achieve higher recognition accuracy than those previous common methods based on deep learning. For instance, our proposed PT-CCNN beats traditional CNN on rotation, scaling and practical deformation datasets by 24.5-49.3%, 9.0-40.8% and 22.3-26.7%. And it also outperforms the polar CNN without using the above techniques on rotation, scaling and practical deformation datasets by 9.2-53.7%, 5.2-54.6% and 9.0-49.9%. Additionally, the presented visualization results show the abilities and advantages of our method in terms of handling image deformation and extracting effective features.INDEX TERMS Automatic target recognition (ATR), inverse synthetic aperture radar (ISAR), deep convolutional neural network (DCNN), image deformation, log-polar transformation.

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Section: A Problem Statementmentioning

confidence: 99%

Section: B Equivariance Property Of Traditional Cnn and Group Convolutionmentioning

confidence: 99%

A Deformation Robust ISAR Image Satellite Target Recognition Method Based on PT-CCNN

et al. 2021

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“…Compared with the traditional Radar Cross-Section (RCS), High-Resolution Range Profile (HRRP), and Micro-Doppler Signature (MDS), the ISAR image can provide more abundant information of the target. Therefore, the use of ISAR images for space target recognition has always been a research hotspot in the field of space situational awareness [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

A Fast Recognition Method for Space Targets in ISAR Images Based on Local and Global Structural Fusion Features with Lower Dimensions

Yang

Zhang

Ding

2020

International Journal of Aerospace Engineering

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Feature extraction is the key step of Inverse Synthetic Aperture Radar (ISAR) image recognition. However, limited by the cost and conditions of ISAR image acquisition, it is relatively difficult to obtain large-scale sample data, which makes it difficult to obtain target deep features with good discriminability by using the currently popular deep learning method. In this paper, a new method for low-dimensional, strongly robust, and fast space target ISAR image recognition based on local and global structural feature fusion is proposed. This method performs the trace transformation along the longest axis of the ISAR image to generate the global trace feature of the space target ISAR image. By introducing the local structural feature, Local Binary Pattern (LBP), the complementary fusion of the global and local features is achieved, which makes up for the missing structural information of the trace feature and ensures the integrity of the ISAR image feature information. The representation of trace and LBP features in a low-dimensional mapping feature space is found by using the manifold learning method. Under the condition of maintaining the local neighborhood relationship in the original feature space, the effective fusion of trace and LBP features is achieved. So, in the practical application process, the target recognition accuracy is no longer affected by trace function, LBP feature block number selection, and other factors, realizing the high robustness of the algorithm. To verify the effectiveness of the proposed algorithm, an ISAR image database containing 1325 samples of 5 types of space targets is used for experiments. The results show that the classification accuracy of the 5 types of space targets can reach more than 99%, and the recognition accuracy is no longer affected by the trace feature and LBP feature selection, which has strong robustness. The proposed method provides a fast and effective high-precision model for space target feature extraction, which can give some references for solving the problem of space object efficient identification under the condition of small sample data.

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Aircraft Target Classification Method for Conventional Narrowband Radar Based on Micro-Doppler Effect

Xia¹,

Zhang²,

Cai³

et al. 2022

Mathematical Problems in Engineering

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For a conventional narrowband radar system, its insufficient bandwidth usually leads to the lack of detectable information of the target, and it is difficult for the radar to classify the target types, such as rotor helicopter, propeller aircraft, and jet aircraft. To address the classification problem of three different types of aircraft target, a joint multifeature classification method based on the micro-Doppler effect in the echo caused by the target micromotion is proposed in this paper. Through the characteristics analysis of the target simulation echoes obtained from the target scattering point model, four features with obvious distinguishability are extracted from the time domain and frequency domain, respectively, that is, flicker interval, fractal dimension, modulation bandwidth, and second central moment. Then, a support vector machine model will be applied to the classification of the three different types of aircraft. Compared with the conventional method, the proposed method has better classification performance and can significantly improve the classification probability of aircraft target. The simulations are carried out to validate the effectiveness of the proposed method.

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Radar Target Recognition using Salient Keypoint Descriptors and Multitask Sparse Representation

Cited by 16 publications

References 8 publications

A Deformation Robust ISAR Image Satellite Target Recognition Method Based on PT-CCNN

A Deformation Robust ISAR Image Satellite Target Recognition Method Based on PT-CCNN

A Fast Recognition Method for Space Targets in ISAR Images Based on Local and Global Structural Fusion Features with Lower Dimensions

Aircraft Target Classification Method for Conventional Narrowband Radar Based on Micro-Doppler Effect

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