Off-grid Imaging Method for Computational Microwave Imaging System of Metamaterial Aperture Based on Sparse Bayesian Learning
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摘要: 基于超材料孔径的计算微波成像可以看作微波压缩感知成像。这种成像方式的成像效果受网格失配误差的严重影响。该文针对超材料孔径计算微波成像系统对2维场景的重构过程进行分析，构建了一种基于Sinc插值函数的2维离网格(Off-grid)观测模型，并在此基础上提出一种基于稀疏贝叶斯学习的Sinc插值离网格成像方法(OGSISBL)。在期望最大化算法的框架下，恢复散射体回波的幅值和位置，同时校准网格失配误差。通过对超材料孔径计算微波成像系统的仿真数据进行成像处理验证所提算法的性能，结果表明所提算法具有很强的鲁棒性。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.
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表 1 比较不同信噪比下本文所提算法对散射体位置的重构效果(3种不同场景)(mm)
设置(场景1) SNR=20 dB SNR=15 dB SNR=10 dB SNR=5 dB 平均离网格误差 运行前 运行后 运行前 运行后 运行前 运行后 运行前 运行后 11.00 5.79 11.00 6.13 11.00 7.26 11.00 14.78 设置(场景2) SNR=20 dB SNR=15 dB SNR=10 dB SNR=5 dB 平均离网格误差 运行前 运行后 运行前 运行后 运行前 运行后 运行前 运行后 5.17 2.76 5.17 3.01 5.17 3.94 5.17 9.02 设置(场景3) SNR=20 dB SNR=15 dB SNR=10 dB SNR=5 dB 平均离网格误差 运行前 运行后 运行前 运行后 运行前 运行后 运行前 运行后 5.38 2.86 5.38 3.18 5.38 4.17 5.38 11.09 
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