An Efficient MMW 3-D Imaging Algorithm for Near-Field MIMO-SAR With Nonuniform Transmitting Array

Chen, Xu; Wang, Hongqiang; Yang, Qi; Zeng, Yang; Deng, Bin

doi:10.1109/lawp.2022.3189417

Cited by 10 publications

(2 citation statements)

References 14 publications

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“…The algorithm leads to outstanding focusing performance and high computational efficiency. Chen et al [14], [15] employed the ADMM algorithm for near-field millimeter-wave (MMW) sparse multipleinput-multiple-output synthetic aperture radar (MIMO-SAR) imaging, enabling high-quality image fast reconstruction under a large dynamic range. In another work [16], ADMM was applied to THz-SAR imaging, showcasing a notable impact on the refocusing of point targets.…”

Section: Introductionmentioning

confidence: 99%

An Equalized ADMM-Based High-Resolution Autofocusing Imaging Algorithm for THz-SAR Ground Moving Targets

Qin,

Deng,

Wang

et al. 2024

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

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Terahertz (THz) radar is well-suited for dynamic mobile target surveillance due to its high frame rates, minimal delay, and superior resolution. However, the defocusing problem caused by the motion of targets in the scene and the nonideal motion of the airborne platform severely affect synthetic aperture radar (SAR) imaging, particularly in the THz wave band. To address this issue, this paper proposes a high-resolution imaging algorithm for THz-SAR ground-moving targets using the equalized alternating direction method of multipliers (ADMM). First, the range-Doppler algorithm (RDA) is employed to generate a coarse image and extract the moving target region as the region of interest (ROI). Then the range profile can be obtained by applying the inverse fast Fourier transform (IFFT), which serves as input for the ADMM solving algorithm. Finally, the 2D image of the target is reconstructed iteratively. The algorithm fully exploits the low-rank and sparse characteristics of moving targets in the SAR image to separate them from the background. It updates the azimuthal matched filter iteratively by minimizing image entropy and designs the equalization factor to retain target details while maintaining image sparsity. This approach significantly improves focusing accuracy compared to conventional algorithms while maintaining high imaging speed without estimating Doppler parameters.

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Section: Introductionmentioning

confidence: 99%

An Equalized ADMM-Based High-Resolution Autofocusing Imaging Algorithm for THz-SAR Ground Moving Targets

Qin,

Deng,

Wang

et al. 2024

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

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“…Previous work has proposed the use of traditional imaging methods for irregular SAR geometric arrays, such as the back-projection algorithm (BPA) [21][22][23] and the nonuniform fast Fourier-transform range-migration algorithm (NUFFT-RMA) [24][25][26][27][28]. These two algorithms belong to the time-domain and frequency-domain imaging algorithms, respectively, which are essentially imaging-optimization algorithms within the framework of matched filtering (MF).…”

Section: Introductionmentioning

confidence: 99%

Near-Field 3D Sparse SAR Direct Imaging with Irregular Samples

Xing

Song²,

Quan³

et al. 2022

Remote Sensing

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Sparse imaging is widely used in synthetic aperture radar (SAR) imaging. Compared with the traditional matched filtering (MF) methods, sparse SAR imaging can directly image the scattered points of a target and effectively reduce the sidelobes and clutter in irregular samples. However, in view of the large-scale computational complexity of sparse reconstruction with raw echo data, traditional sparse reconstruction algorithms often require huge computational expense. To solve the above problems, in this paper, we propose a 3D near-field sparse SAR direct imaging algorithm for irregular trajectories, adopting a piece of preliminary information in the SAR image to update the dictionary matrix dimension, using the Gaussian iterative method, and optimizing the signal-processing techniques, which can achieve 3D sparse reconstruction in a more direct and rapid manner. The proposed algorithm was validated through simulations and empirical study of irregular scanning scenarios and compared with traditional MF and sparse reconstruction methods, and was shown to significantly reduce the computation time and effectively preserve the complex information of the scenes to achieve high-resolution image reconstruction.

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Near-Field Low-Storage SAR Imaging With Spatio-Temporal Sub-Nyquist Samples

Peng,

Sheng,

2024

IEEE Sensors J.

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An Efficient MMW 3-D Imaging Algorithm for Near-Field MIMO-SAR With Nonuniform Transmitting Array

Cited by 10 publications

References 14 publications

An Equalized ADMM-Based High-Resolution Autofocusing Imaging Algorithm for THz-SAR Ground Moving Targets

An Equalized ADMM-Based High-Resolution Autofocusing Imaging Algorithm for THz-SAR Ground Moving Targets

Near-Field 3D Sparse SAR Direct Imaging with Irregular Samples

Near-Field Low-Storage SAR Imaging With Spatio-Temporal Sub-Nyquist Samples

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