Generation of High-Quality Spaceborne Interrupted FMCW SAR Images via Singular Value Threshold-Based Matrix Completion

Liu, Xiangqian; Li, Ning; Shu, Gaofeng; Lin, Min

doi:10.1109/lgrs.2022.3157466

Cited by 6 publications

(4 citation statements)

References 13 publications

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“…Subproblems Equations (20) and (21) are solved is based on two previously proven results [30].

{S}_{\varepsilon }[\mathbf{Y}]=\underset{\mathbf{X}}{\mathrm{arg}\hspace*{.5em}\text{min}}\ \varepsilon {{\Vert}\mathbf{X}{\Vert}}_{1}+\frac{1}{2}{{\Vert}\mathbf{X}-\mathbf{Y}{\Vert}}_{F}^{2}

\mathbf{U}{S}_{\varepsilon }[\mathbf{S}]{\mathbf{V}}^{T}=\underset{\mathbf{X}}{\mathrm{arg}\hspace*{.5em}\text{min}}\ \varepsilon {{\Vert}\mathbf{X}{\Vert}}_{\ast }+\frac{1}{2}{{\Vert}\mathbf{X}-\mathbf{Y}{\Vert}}_{F}^{2}

where

\mathbf{U}\mathbf{S}{\mathbf{V}}^{\mathrm{H}}

is the SVD decomposition result of

\mathbf{Y}

, which is only used to solve the first subproblem about key nodes of data matrix

\mathbf{R}

.…”

Section: Moving Target Detection Methods Based On Cur‐rpcamentioning

confidence: 99%

“…The much smaller matrices can be obtained through the target low‐rank matrix factorization, and then the computational complexity is reduced by replacing original matrix with the much smaller matrices to perform MC. For example, in [21], the missing data is recovered with high precision by MC, which based on singular value threshold. However, the computational complexity of SVT‐MC is still not satisfactory.…”

Section: Introductionmentioning

confidence: 99%

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Moving target detection method based on CUR‐RPCA for missing array elements

Shi

Guo

Hua

et al. 2022

IET Radar Sonar & Navi

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Moving target detection performance is seriously affected by missing array elements for multichannel synthetic aperture radar (Multi-SAR) systems. Meanwhile, there is a contradiction between the accuracy of data recovery and the computational burden in traditional methods. To solve the problems, a moving target detection method based on CUR-RPCA for missing array elements is proposed in this paper. First, key nodes of the echo data are extracted and rearranged into a low-rank matrix. Then we uses the low-rank characteristic to complete key nodes of data. Subsequently, complete echo data is constructed by utilising recovered key nodes of data and CUR decomposition. Therefore using the ideas of CUR and RPCA, a novel optimization problem is constructed to achieve separation of moving targets and clutter while recovering data. In addition, only key nodes of the data is completed to achieve the performance of all data recovery, so that the disadvantage of low computational efficiency has been overcome for traditional matrix completion methods. Finally, the superior clutter suppression performance of the proposed method is verified by several numerical simulations and comparative studies.

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“…Subproblems Equations (20) and (21) are solved is based on two previously proven results [30].

{S}_{\varepsilon }[\mathbf{Y}]=\underset{\mathbf{X}}{\mathrm{arg}\hspace*{.5em}\text{min}}\ \varepsilon {{\Vert}\mathbf{X}{\Vert}}_{1}+\frac{1}{2}{{\Vert}\mathbf{X}-\mathbf{Y}{\Vert}}_{F}^{2}

\mathbf{U}{S}_{\varepsilon }[\mathbf{S}]{\mathbf{V}}^{T}=\underset{\mathbf{X}}{\mathrm{arg}\hspace*{.5em}\text{min}}\ \varepsilon {{\Vert}\mathbf{X}{\Vert}}_{\ast }+\frac{1}{2}{{\Vert}\mathbf{X}-\mathbf{Y}{\Vert}}_{F}^{2}

where

\mathbf{U}\mathbf{S}{\mathbf{V}}^{\mathrm{H}}

is the SVD decomposition result of

\mathbf{Y}

, which is only used to solve the first subproblem about key nodes of data matrix

\mathbf{R}

.…”

Section: Moving Target Detection Methods Based On Cur‐rpcamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Moving target detection method based on CUR‐RPCA for missing array elements

Shi

Guo

Hua

et al. 2022

IET Radar Sonar & Navi

View full text Add to dashboard Cite

show abstract

“…The synthesized spectrum is shown in (18). It can be seen that, compared with (7), the phases of subband…”

Section: E Wideband Synthesismentioning

confidence: 99%

“…On one hand, the implementation of high range resolution requires spaceborne SAR to have the ability of transmitting and receiving ultra-wideband (UWB) signals. Limited by the current hardware technology, however, such an excessive demand for signal bandwidth will bring great difficulties to the system hardware, in which case new technologies like frequency stepping or frequency modulated continuous wave (FMCW) [6], [7] have almost become a fixture of ultra-highresolution SAR.…”

Section: Introductionmentioning

confidence: 99%

An Improved Ultrahigh-Resolution Stepped-Frequency Spaceborne SAR Imaging Algorithm

Li,

Ding,

Zhang

et al. 2024

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

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Frequency stepping is a widely used technique for ultra-high-resolution synthetic aperture radar (SAR). Although reducing the burden of hardware, the technique increases the complexity of imaging algorithms due to the inter-subband time offsets and inter-subband errors of delay, amplitude and phase. To address the above problems, an improved ultra-highresolution stepped-frequency spaceborne SAR imaging algorithm is proposed in this paper. By generating subband images individually, performing inter-subband error estimation based on primary points, and then synthesizing the subband images in the imaging domain, the proposed algorithm effectively avoids the problem of time offsets and significantly improves inter-subband error compensation accuracy benefiting from the high SNR in the imaging domain. Besides, considering the characteristics of non-ideal factors in frequency-stepped SAR, a series of error compensation methods aiming at stop-and-go approximation, ionospheric error and tropospheric delay are integrated to the proposed algorithm. The effectiveness of the proposed algorithms is verified via computer simulations, and real data experiments are also conducted based on both an X-band spaceborne SAR system, Taijing 4-01, and a Ka-band spaceborne SAR system, Luojia 2-01.

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