Millimeter-wave imaging systems are increasingly used in human screening at checkpoints, in linear or multi-input multi-output (MIMO) array modes. Rather than pressing intrinsic system requirements to improve image quality, it seems more economical to appeal to some specific approaches, for example, super-resolution (SR) techniques. In this paper, as a variation of SR, coherence factor (CF) is incorporated in the framework of the range migration algorithm (RMA), termed CF-RMA for short. As a critical part of CF, the double integral in the wavenumber domain is approximated by a single integral in the sense of the point-matching method, resulting in a particular formulation to carry on another independent RMA program. Being computationally efficient, the proposed method is shown to be effective in suppressing sidelobes and background noise. The resolution (as gauged by the width of the main lobe of the point spread function) is enhanced as well. A structural similarity (SSIM) index is employed to quantify the image quality enhancement with respect to state-of-the-art SR approaches. In terms of computation load, the new approach is shown to be much more efficient than state-of-the-art SR approaches.

Millimeter wave is widely used in concealed weapon detection for its capability of penetrating clothing, especially in the low-frequency region. Compared with the monostatic modality, multi-input multi-output (MIMO, also termed multi-static) array imaging is characterized by real-time sampling and diversity. In this article, a millimeter-wave imaging testbed in the Ka-band with MIMO array is developed to reconstruct 3-D images. The magnetoelectric (ME) dipole with the dielectric substrate integrated waveguide (SIW) structure is used in antenna design, which is characterized by low transmission loss, convenience for integration, and broad beam radiation in a wide frequency band. A modified MIMO back projection algorithm (BPA) is exploited to achieve high-precision imaging. To accommodate the system, a two-step calibrating approach is proposed using a simple attenuator. As demonstrated with the simulation and experimental results, the proposed calibration method outperforms a state-of-the-art convex-optimization-based one in terms of computational efficiency and image quality, which is assessed with a structural similarity (SSIM) index quantitatively.

Moving target detection in the disturbing environment has been significantly active and challenging for underwater acoustic array signal processing. One of the most effective approaches is based on low-rank matrix estimation, which divides the reconstructed imaging frames into low-rank and sparse matrix components, with the low-rank structure representing the interference and the sparse component corresponding to the moving target. However, the array model has not been well explored, taking the reconstruction and moving target separation as independent steps. In this paper, the sparse Bayesian learning (SBL) approach is employed to recover the stable interference and the moving target simultaneously and separately, retaining the high reconstruction quality pertaining to SBL. Specifically, in each frame, a shared prior distribution is assigned to the stable interference, while the time-varying component (the moving target) is modeled with a unique prior distribution. The hierarchical model is solved by the variational Bayesian inference. The superior performance of the proposed algorithm is validated by simulation and experimental data in an underwater acoustic test tank and Songhua Lake in China, compared with state-of-the-art low-rank matrix estimation algorithms.

Direction of arrival (DOA) and source amplitude estimation are widely encountered in detection and communication as traditional array processing problems. Recently, sparse Bayesian learning (SBL) technique is increasingly exploited, which outperforms conventional compressive sensing (CS) approaches and provides an improved alternative for DOA and amplitude estimation. Although the accuracy of DOA is enhanced somewhat through SBL, the on-grid implementation of SBL suffers the grid mismatch errors due to predefined grids. The off-grid SBL can be employed to address the issue. However, the computation load of off-grid SBL is heavy because of per-iteration matrix inversion and slow convergence, which restricts its application for online systems. An off-grid space alternating multi-snapshot SBL (OGSA-MSBL) approach used for DOA and source amplitude estimation is proposed in this paper, which introduces the offset between the ground-truth DOA and the grid nodes to refine the model, and employs the space alternating (SA) approach to solve the problem efficiently. Compared with the original SA, the DOA accuracy can be improved even with a reduced number of grids, while the source amplitude can be estimated with a higher precision with the first-order Taylor refined beamforming model. As shown by the simulation and experimental results, the recovery accuracy of the proposed method is superior to the state-of-the-art on-grid SBL approaches and comparable to its off-gird counterpart, while much faster in terms of computation efficiency.

Polypropylene (PP) is a widely used common type of polymer with excellent chemical and mechanical properties. Thermal aging is a main culprit in degrading its lifetime and effectiveness. In this work, terahertz dielectric frequency response of PP during the process of thermal aging is monitored and analyzed to understand the underlying mechanism, and to qualify terahertz (THz) dielectric spectroscopy as a useful tool for time-stamping and benchmarking thermal aging of polymer materials. Using an improved THz material parameter extraction algorithm for thin films, the real and imaginary parts of the dielectric permittivity of standard PP test coupons were obtained at various stages of the thermal aging process. In agreement with theoretical analysis based on a molecular-level understanding of the thermal aging dynamics, the experimental results indicated that THz wave transmission loss of PP correlated quantitatively with the degree of its thermal aging.

To reduce the adverse effects of propagation delay uncertainty caused phase error, a pragmatic self-calibration approach for millimeter wave MIMO imaging is proposed and demonstrated. Based on the analysis of the contributions of delay uncertainty and the assumption of identical channel design, the direct coupling between array elements is used to estimate the hardware-caused inherent channel delay, requiring no additional reference aid. Of the contributions to the delay, the common part and the part due to the inter-channel difference are estimated separately. Combined with the array geometrical configuration, the direct coupling reflections are averaged to find the common channel delay; and the inter-channel differences are estimated in a reduced geometric average scheme. The delay uncertainty is compensated by performing a phase shift operation to the received echoes for each channel. Additionally, the proposed method also allows the accommodation of the slow change of the channel inherent delay automatically and constantly. Numerical analysis and experimental tests demonstrate the projected performance improvement of the proposed scheme as an alternative approach, which can also serve as a benchmark or booster for reference aided phase calibration methods.