ISAR High-Resolution Imaging Method With Joint FISTA and VGGNet

Xu, Wenjie; Yang, Jun; Lv, Mingjiu; Chen, Wenfeng; Xiao-yan, MA; Long, Ming; Xia, Saiqiang

doi:10.1109/access.2021.3086980

Cited by 14 publications

(8 citation statements)

References 21 publications

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“…In general, the target rotates with small angle during short CPI. Let the reference distance meet: R ref = R 0 , R i (t m ) can be approximated as [1]:…”

Section: Signal Modelmentioning

confidence: 99%

“…Due to the non-cooperation and structural characteristics of the imaging target, the number of scattering points of Z in different range cells is usually different, and the support set after the sparse representation of S also changes over time. Therefore, the solution of Z is an arbitrary sparse structure reconstruction problem [1]. In order to solve (13) more effectively, RJFOMP algorithm is proposed based on OMP algorithm.…”

Section: ) Rjfomp Algorithmmentioning

confidence: 99%

“…Benefiting from the ability of long-distance, all-time and all-weather detection for non-cooperative moving targets, inverse synthetic aperture radar (ISAR) plays an important role in military and civilian fields such as strategic early warning, space surveillance and target recognition [1]. With respect to ISAR imaging, the range resolution is related to the bandwidth of the radar transmitted signal, and the azimuth resolution depends on the coherent processing interval (CPI) and the angle of the target relative to the radar line of sight (RLOS) [2].…”

Section: Introductionmentioning

confidence: 99%

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Translational Motion Compensation for ISAR Imaging Based on Range Joint Fast Orthogonal Matching Pursuit Algorithm

Xu¹,

Yang²,

Lv³

et al. 2022

IEEE Access

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Accurate translational motion compensation (TMC) is the key procedure of inverse synthetic aperture radar (ISAR) imaging. Under the condition of sparse aperture, the correlation of adjacent pulses is severely destroyed, which brings a few challenges to TMC and imaging. Thus, this paper proposes a novel TMC method for ISAR imaging. In this technique, the signal model of joint phase adjustment and ISAR imaging is established first. Then, the process of TMC is designed. That is, after range alignment with improved global minimum entropy (IGME), the range joint fast orthogonal matching pursuit (RJFOMP) algorithm is applied to ISAR imaging, and the minimum entropy method (MEM) is adopted to estimate the phase error. Meanwhile, the joint optimization of phase adjustment and ISAR imaging is realized through the cyclic iteration. Extensive experiments based on both simulated and measured data demonstrate that the proposed TMC method is effective for two modes of motion errors, which are named as coherent mode (CM) and non-coherent mode (NCM), respectively. When RJFOMP is used for joint phase adjustment and ISAR imaging, it has excellent imaging performance under noisy and sparse conditions.INDEX TERMS Inverse synthetic aperture radar (ISAR), translational motion compensation (TMC), improved global minimum entropy (IGME), range joint fast orthogonal matching pursuit (RJFOMP) algorithm, minimum entropy method (MEM).

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“…In general, the target rotates with small angle during short CPI. Let the reference distance meet: R ref = R 0 , R i (t m ) can be approximated as [1]:…”

Section: Signal Modelmentioning

confidence: 99%

Section: ) Rjfomp Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Translational Motion Compensation for ISAR Imaging Based on Range Joint Fast Orthogonal Matching Pursuit Algorithm

Xu¹,

Yang²,

Lv³

et al. 2022

IEEE Access

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“…A super-resolution ISAR imaging method based on deep-learning-assisted time-frequency analysis (TFA) is proposed in [14], in which the basic structure of the neural network also adopts the U-net. In [15], the author proposes a joint FISTA and Visual Geometry Group Network (VGGNet) ISAR imaging method to improve imaging performance and reduce complexity. In [16], the author notices that many articles use the generative adversarial network (GAN) for SAR image translation, while few articles apply GAN to ISAR imaging.…”

Section: Introductionmentioning

confidence: 99%

ISAR Resolution Enhancement Method Exploiting Generative Adversarial Network

Wang

Lu³

et al. 2022

Remote Sensing

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Deep learning has been used in inverse synthetic aperture radar (ISAR) imaging to improve resolution performance, but there still exist some problems: the loss of weak scattering points, over-smoothed imaging results, and the universality and generalization. To address these problems, an ISAR resolution enhancement method of exploiting a generative adversarial network (GAN) is proposed in this paper. We adopt a relativistic average discriminator (RaD) to enhance the ability of the network to describe target details. The proposed loss function is composed of feature loss, adversarial loss, and absolute loss. The feature loss is used to get the main characteristics of the target. The adversarial loss ensures that the proposed GAN recovers more target details. The absolute loss is adopted to make the imaging results not over-smoothed. Experiments based on simulated and measured data under different conditions demonstrate that the proposed method has good imaging performance. In addition, the universality and generalization of the proposed GAN are also well verified.

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“…As an advanced means of remote sensing, inverse synthetic aperture radar (ISAR) can realize two-dimensional highresolution imaging of the noncooperative moving target, in which the range and azimuth resolutions depend on the bandwidth of radar transmitted signal and the rotation angle of the target relative to radar line of sight (RLOS), respectively [1]. With the maturity of ISAR imaging technology, ISAR plays a more and more important role in military and civil elds.…”

Section: Introductionmentioning

confidence: 99%

Removal of Micro-Doppler Effects in ISAR Imaging Based on the Joint Processing of Singular Value Decomposition and Complex Variational Mode Extraction

Xu¹,

Yang²,

Lv³

et al. 2022

Mathematical Problems in Engineering

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For inverse synthetic aperture radar (ISAR) imaging of targets with micromotion parts, the removal of micro-Doppler (m-D) effects is the key procedure. However, under the condition of a sparse aperture, the echo pulse is limited or incomplete, giving rise to the difficulty of eliminating m-D effects. Thus, a novel m-D effects removal algorithm is proposed, which can effectively eliminate m-D effects, as well as the interference introduced by noise and sparse aperture in the ISAR image of the main body. The proposed algorithm mainly includes two processing steps. First, combined with the cut-off point determined by the normalized singular value difference spectrum, the rough estimation of the main body is achieved by singular value decomposition (SVD). Then, the variational mode extraction (VME) is extended to complex variational mode extraction (CVME). The constrained variational problem constructed by bandwidth and spectral overlap constraints is solved by the alternating direction method of multipliers (ADMM), and the precise estimation of the main body is obtained. Experimental results based on both simulated and measured data demonstrate that the proposed algorithm can acquire the high-resolution ISAR image of the main body under noisy and sparse conditions.

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ISAR High-Resolution Imaging Method With Joint FISTA and VGGNet

Cited by 14 publications

References 21 publications

Translational Motion Compensation for ISAR Imaging Based on Range Joint Fast Orthogonal Matching Pursuit Algorithm

Translational Motion Compensation for ISAR Imaging Based on Range Joint Fast Orthogonal Matching Pursuit Algorithm

ISAR Resolution Enhancement Method Exploiting Generative Adversarial Network

Removal of Micro-Doppler Effects in ISAR Imaging Based on the Joint Processing of Singular Value Decomposition and Complex Variational Mode Extraction

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