Off-grid Imaging Method for Computational Microwave Imaging System of Metamaterial Aperture Based on Sparse Bayesian Learning

FU Haosheng; HONG Ling; DAI Fengzhou

doi:10.11999/JEIT220363

Volume 44 Issue 12

Dec. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(12): 4075-4084

FU Haosheng, HONG Ling, DAI Fengzhou. Off-grid Imaging Method for Computational Microwave Imaging System of Metamaterial Aperture Based on Sparse Bayesian Learning[J]. Journal of Electronics & Information Technology, 2022, 44(12): 4075-4084. doi: 10.11999/JEIT220363

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FU Haosheng, HONG Ling, DAI Fengzhou. Off-grid Imaging Method for Computational Microwave Imaging System of Metamaterial Aperture Based on Sparse Bayesian Learning[J]. Journal of Electronics & Information Technology, 2022, 44(12): 4075-4084. doi: 10.11999/JEIT220363

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Off-grid Imaging Method for Computational Microwave Imaging System of Metamaterial Aperture Based on Sparse Bayesian Learning

doi: 10.11999/JEIT220363

1.
National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China
2.
School of Computer Science, Shaanxi Normal University, Xi’an 710062, China

Received Date: 2022-03-31
Rev Recd Date: 2022-07-05

Available Online: 2022-07-11

Publish Date: 2022-12-16

Abstract

Abstract

Computational microwave imaging based on metamaterial aperture can be considered as microwave compression sensing imaging. The imaging effect of this imaging method is seriously affected by the grid mismatch error. In this paper, a Two-Dimensional (2D) off-grid observation model based on Sinc interpolation function is constructed by analyzing the reconstruction process of 2D scene in the computational microwave imaging system for metamaterial aperture. On this basis, an Off-Grid imaging method using Sinc Interpolation based on Sparse Bayesian Learning (OGSISBL) is proposed. Under the framework of the expectation maximization algorithm, the amplitude and position of the return of the scatterers are recovered, and the off-grid error is calibrated. The performance of the proposed algorithm is verified by imaging the simulation data of the computing microwave imaging system based on metamaterial aperture. The results show that the proposed algorithm has strong robustness.
- Computational microwave imaging,
- Metamaterial aperture,
- Off-grid error,
- Sparse Bayesian learning

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References(21)

References

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