Near Field Image Reconstruction Algorithm for Passive Millimeter-Wave Imager Bhu-2d-U

Abstract

Abstract-A passive millimeter-wave imager BHU-2D-U based on synthetic aperture interferometric radiometer (SAIR) technique has been developed by Beihang University. The imager is designed for detecting concealed weapons on human body and operated under the near-field condition of the antenna array, thus the conventional Fourier imaging theory does not apply. In this paper, an accurate numerical image reconstruction algorithm using regularization theory is proposed. By means of adding a prior information of desired brightness temperature image, the influences of measurement noise and focusing error on the reconstructed image have been reduced. Numerical simulations and experiments on BHU-2D-U have been performed to verify the superiorities of the proposed algorithm over the corrected Fourier method and the Moore-Penrose pseudo inverse method. The results demonstrate that the proposed method is an advantageous imaging algorithm for near-field millimeter-wave SAIR.

Synthetic aperture interferometric imaging using a passive microwave coding device

“…This is the near-field Fourier imaging which can be applied to reconstruct a point-like radiating source. An accurate imaging algorithm can be used for a distributed source imaging [7]- [8]. Nevertheless, high resolution imaging requires a large number of antennas which is directly linked to the number of receivers leading to a cost and complex hardware.…”

Regularization Imaging Algorithm With Accurate G Matrix for Near-Field MMW Synthetic Aperture Imaging Radiometer

“…For the purpose of the variables separation, the distances R c n and R l n are usually unfolded by the Taylor expansion approximately [6,7,[16][17][18][19]. Then the difference between R c n and R l n can be expressed as…”

“…Besides, the dominant noises in SAIR images are usually the Gaussian white noises. Under this circumstance, the regularization is one of the most effectual methods to estimate the unique and stable T R , and the satisfying recovery effects are achieved in near-field imaging [18,19]. In this paper, we estimate T R by minimizing the following function.…”

“…The corresponding parameters are then corrected according to the root-mean square error (RMSE) between the imaging results of the reconstruction method and modified FFT method. Because only the phase-modified item is added to the FFT, which can be seen as the focusing operation for the MMW SAIR [17][18][19], the modified FFT method (one kind of the FFT-based methods) is insensitive to the change of those corresponding parameters. For verifying the effectiveness of the proposed accurate G matrix, the traditional regularization with the approximate G matrix is also tested for comparison.…”

“…Because regularization reconstructions of the SAIR with sparse arrays are an underdetermined problem, the accuracy of operation matrix G is very important for the reconstruction process. Now, the matrixes Gs are mostly established from the forward analysis of SAIR imaging process [15][16][17][18]. For the purpose of variables separation, some unnecessary approximations are adopted to simplify the G matrixes in different degrees.…”