Millimeter wave synthetic aperture interferometric radiometers (SAIR) are very powerful instruments, which can effectively realize high-precision imaging detection. However due to the existence of interference factor and complex near-field error, the imaging effect of near-field SAIR is usually not ideal. To achieve better imaging results, a new fully connected imaging network (FCIN) is proposed for near-field SAIR. In FCIN, the fully connected network is first used to reconstruct the image domain directly from the visibility function, and then the residual dense network is used for image denoising and enhancement. The simulation results show that the proposed FCIN method has high imaging accuracy and shorten imaging time.

Aperture synthesis technology represents an effective approach to millimeter-wave radiometers for high-resolution observations. However, the application of synthetic aperture imaging radiometer (SAIR) is limited by its large number of antennas, receivers and correlators, which may increase noise and cause the image distortion. To solve those problems, this letter proposes a compressive regularization imaging algorithm, called CRIA, to reconstruct images accurately via combining the sparsity and the energy functional of target space. With randomly selected visibility samples, CRIA employs l1 norm to reconstruct the target brightness temperature and l2 norm to estimate the energy functional of it simultaneously. Comparisons with other algorithms show that CRIA provides higher quality target brightness temperature images at a lower data level.

Millimeter-wave synthetic aperture imaging radiometer (SAIR) is a powerful sensor for near-field high-resolution observations. However, the large receiver number and system complexity affect the application of SAIR. To overcome this shortage (receiver number), an accurate imaging algorithm based on compressed sensing (CS) theory is proposed in this paper. For reconstructing the brightness temperature images accurately from the sparse SAIR with fewer receivers, the proposed CS-based imaging algorithm is used to accomplish the sparse reconstruction with fewer visibility samples. The reconstruction is performed by minimizing the $l_{1}$ norm of the transformed image. Compared to the FFT-based methods based on Fourier transform, the required receiver number can be further reduced by this method. The simulation results demonstrate that the proposed CS-based method has higher reconstruction accuracy for the sparse SAIR.

This paper proposes new techniques to simulate a MIMO propagation channel using the ray-tracing method for the purpose of decreasing the computational complexity. These techniques simulate a MIMO propagation channel by substituting the propagation path between a particular combination of transmitter and receiver antennas for all combinations of transmitter and receiver antennas. The estimation accuracy calculated using the proposed techniques is evaluated based on comparison to the results calculated using imaging algorithms. The results show that the proposed techniques simulate a MIMO propagation channel with low computational complexity, and a high level of estimation accuracy is achieved using the proposed Vector-Rotation Approximation technique compared to that for the imaging algorithm.