Joint ISAR Imaging and Cross-Range Scaling Method Based on Compressive Sensing With Adaptive Dictionary

Jiu, Bo; Liu, Hongchao; Liu, Hongwei; Zhang, Lei; Yang, Cong; Bao, Zheng

doi:10.1109/tap.2015.2409876

Cited by 22 publications

(3 citation statements)

References 33 publications

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“…Real measured data of Yak-42 [7,30] is utilized to further analyze the performance of the proposed algorithm. Yak-42 is a twin engine commercial aircraft with a size of 36 m × 35 m, and during the interval of ISAR imaging, it is flying smoothly at an approximate speed of 380 km/h.…”

Section: Resultsmentioning

confidence: 99%

Logarithmic Laplacian Prior Based Bayesian Inverse Synthetic Aperture Radar Imaging

Zhang

Liu

et al. 2016

Sensors

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This paper presents a novel Inverse Synthetic Aperture Radar Imaging (ISAR) algorithm based on a new sparse prior, known as the logarithmic Laplacian prior. The newly proposed logarithmic Laplacian prior has a narrower main lobe with higher tail values than the Laplacian prior, which helps to achieve performance improvement on sparse representation. The logarithmic Laplacian prior is used for ISAR imaging within the Bayesian framework to achieve better focused radar image. In the proposed method of ISAR imaging, the phase errors are jointly estimated based on the minimum entropy criterion to accomplish autofocusing. The maximum a posterior (MAP) estimation and the maximum likelihood estimation (MLE) are utilized to estimate the model parameters to avoid manually tuning process. Additionally, the fast Fourier Transform (FFT) and Hadamard product are used to minimize the required computational efficiency. Experimental results based on both simulated and measured data validate that the proposed algorithm outperforms the traditional sparse ISAR imaging algorithms in terms of resolution improvement and noise suppression.

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Section: Resultsmentioning

confidence: 99%

Logarithmic Laplacian Prior Based Bayesian Inverse Synthetic Aperture Radar Imaging

Zhang

Liu

et al. 2016

Sensors

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“…For the space targets, however, it is difficult to obtain sufficient prior information due to the time-varying IPP. In the aspect of ISAR image deformable correction, the azimuth scaling, which could lay a foundation for ISAR image ATR, has received intensive attention in recent years [31], [32]. However, the successful application of the relevant techniques depends strictly on the instantaneous slant range model, the number of dominant scattering centers in a range cell and Doppler chirp rate, which may not be satisfied in real-world situations.…”

Section: Related Workmentioning

confidence: 99%

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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“…Reference [365] presents comparisons between CS and other super-resolution techniques in ISAR. A number of different CS methods have been used for this purpose: sparse Bayesian learning [366]- [373], the RELAX method [374], matching pursuit [375], and dictionary learning [376], [377].…”

Section: A Cs In Isarmentioning

confidence: 99%

Inverse Synthetic Aperture Radar Imaging: A Historical Perspective and State-of-the-Art Survey

Vehmas¹,

Neuberger

2021

IEEE Access

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Radar can obtain high spatial resolution at large operating distances and its operation is largely independent from weather and lighting conditions. This makes it an important instrument in an ever-increasing number of applications requiring robust and reliable sensing of the environment. Due to the advancements in semiconductor technology and digital computation power, radar can obtain increasingly better position estimation accuracy, detection sensitivity, and target resolution. High-resolution imaging is an example of a rapidly advancing field of radar technology. Inverse synthetic aperture radar (ISAR) is an imaging technique that uses the motion of a target to obtain a high-resolution radar image of it. This paper presents a comprehensive review of past work and the state-of-the-art in ISAR imaging systems, algorithms, and applications. Starting from the early historical developments of simple monostatic range-Doppler imaging, we present the developments up to the current advanced spatially distributed imaging modalities and reconstruction algorithms. The list of references includes the most influential ISAR publications in the open literature up to early 2021.

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Joint ISAR Imaging and Cross-Range Scaling Method Based on Compressive Sensing With Adaptive Dictionary

Cited by 22 publications

References 33 publications

Logarithmic Laplacian Prior Based Bayesian Inverse Synthetic Aperture Radar Imaging

Logarithmic Laplacian Prior Based Bayesian Inverse Synthetic Aperture Radar Imaging

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

Inverse Synthetic Aperture Radar Imaging: A Historical Perspective and State-of-the-Art Survey

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