A Three-Dimensional Imaging Algorithm of Downward-looking Sparse Linear Array SAR Based on Low-rank Tensor

Siqian ZHANG; Meiting YU; Gangyao KUANG

doi:10.11999/JEIT200274

Volume 43 Issue 6

Jun. 2021

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Article Navigation > Journal of Electronics & Information Technology > 2021 > 43(6): 1667-1675

Siqian ZHANG, Meiting YU, Gangyao KUANG. A Three-Dimensional Imaging Algorithm of Downward-looking Sparse Linear Array SAR Based on Low-rank Tensor[J]. Journal of Electronics & Information Technology, 2021, 43(6): 1667-1675. doi: 10.11999/JEIT200274

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Siqian ZHANG, Meiting YU, Gangyao KUANG. A Three-Dimensional Imaging Algorithm of Downward-looking Sparse Linear Array SAR Based on Low-rank Tensor[J]. Journal of Electronics & Information Technology, 2021, 43(6): 1667-1675. doi: 10.11999/JEIT200274

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A Three-Dimensional Imaging Algorithm of Downward-looking Sparse Linear Array SAR Based on Low-rank Tensor

doi: 10.11999/JEIT200274

1.
State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, National University of Defense Technology, Changsha 410073, China
2.
Engineering Research Center for Position, Navigation and Time, National University of Defense Technology, Changsha 410073, China

Funds: The Natural National Science Foundation of China (61701508), The Natural Science Foundation of Hunan Province (2018JJ3613)

Received Date: 2020-04-17
Rev Recd Date: 2020-11-21

Available Online: 2020-11-25

Publish Date: 2021-06-18

Abstract

Abstract

In order to solving the problems of the inner structure damage and the high computation load brought by the vectorizing or matrixing of 3-D sparse data, the 3-D signal model is established in tensor space for downward-looking sparse linear array three-dimensional SAR. Based on this signal model, a three-dimensional SAR sparse imaging algorithm is proposed in this paper. The missing data firstly can be recovered by tensor completion on the assumption that the echo tensor is essentially low rank. Then, the resulting 3-D images can be well focused by any Fourier transform-based 3-D imaging algorithms with the recovered full-sampled data tensor. The proposed algorithm achieves not only high resolution and low-level side-lobes but also the ideal computational cost and memory consumption, which verified by several numerical simulations and multiple comparative studies on real data.
- Synthetic Aperture Radar(SAR),
- 3-D imaging,
- Downward-looking,
- Sparse reconstruction,
- Tensor completion

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References(17)

References

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