Three‐dimensional SAR imaging of sea targets with low PRF sampling

Wang, Pengfei; Liu, Mei; Wang, Zhigui

doi:10.1049/iet-rsn.2017.0363

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…Synthetic Aperture Radar (SAR) can achieve high-resolution imaging of moving target and background [1][2][3][4]. Thus, SAR-GMTI systems are widely utilised in applications such as ocean, traffic control, disaster and battlefield surveillance.…”

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

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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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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

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“…Although full 3-D imaging is an advisable way, using Fourier processing methods, such as 3-D non-uniform fast Fourier transform (3-D NUFFT), generates poor imaging results with high side-lobes due to the sparsity of the k-space samples [17,18]. Sparse reconstruction is a method of model matching, in which regularization enforcing sparsity to obtain sparse results [19,20]. Combining the sparse reconstruction with the sparsity of the data to carry out 3-D sparse imaging for non-uniform samples is expected to reduce high side-lobes and obtain high-resolution imaging results.…”

Section: Introductionmentioning

confidence: 99%

Direct 3-D Sparse Imaging Using Non-Uniform Samples Without Data Interpolation

Pang

Xing

et al. 2020

Electronics

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As an emerging technique, sparse imaging from three-dimensional (3-D) and non-uniform samples provides an attractive approach to obtain high resolution 3-D images along with great convenience in data acquisition, especially in the case of targets consisting of strong isolated scatterers. Although data interpolation in k-space and fast Fourier transform have been employed in the existing 3-D sparse imaging methods to reduce the computational complexity, the data-gridding errors induced by local interpolation may usually result in poor imaging performance. In this paper, we directly regard the imaging problem as a joint sparse reconstruction problem from non-uniform data without interpolation in 3-D space. Combining dictionary reduction and Gauss iterative method with the optimized signal processing scheme, a sparse imaging algorithm is proposed to address the difficulty of large-scale computation involved in direct 3-D sparse reconstruction. Benefited from the optimized signal processing scheme and the avoidance of data interpolation, the direct 3-D sparse imaging (DTDSI) method proposed in this paper is of low computation scale and high imaging performance. Experiments of electromagnetic simulation data demonstrate the DTDSI method outperforms baseline methods in terms of resolving ability, lower side-lobes and higher accuracy.

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“…Synthetic aperture radar (SAR) is capable of generating high‐resolution microwave images of ground/sea scenes in all illumination/weather conditions [1–4]. Different from inverse SAR (ISAR) whose primary mission is to image the air/space moving targets, the main mission of SAR is to image stationary scenes.…”

Section: Introductionmentioning

confidence: 99%