Synthesis of thinned linear array geometry with low redundancy is an important problem for one-dimensional aperture synthesis radiometers (ASRs) with high spatial resolution and satisfactory sensitivity. In this paper, a synthesis method of low-redundancy linear array (LRLA) with large numbers of antennas is proposed. Two LRLA patterns are suggested to significantly reduce the tremendous solution space of large LRLA search problem and a strategy is proposed to speed up exploration. Further, particle swarm optimization (PSO) algorithm exploiting the suggested patterns and strategy is applied to the array synthesis. The advantages of the proposed method, in terms of both generating more new large LRLA configurations and reducing computational cost for the large LRLA configuration synthesis problem, are demonstrated by comparing its performance with that of the previously proposed methods. Finally, a number of newly found large LRLA configurations for 28-37 elements are also presented.

Polarimetric passive millimeter-wave (PMMW) measurements can provide additional information about scenes. We report on the development of a 94GHz Multi-Polarization Scanning Imaging Radiometer (MPSIR) for outdoor imaging applications, and the polarization characteristic analysis of experimentally acquired images. The imager angular resolution is measured to be 0.37°, and the thermal sensitivity is about 0.46K. Multi-polarization measurements are realized by rotating the detector around the observation axis direction. Several outdoor complex scenes are selected to passive image, which include the outdoor parking lot and the dormitory buildings in the woods. Various polarization parameter images are generated to analyze the polarization characteristics. Based on the multi-polarization imaging, eight more polarization parameters are created and they are all have the special distribution properties. The experimental results indicate that the multi-polarization imaging and the modified polarization parameters have the great potential for three-dimensional reconstruction, edge detection and image segmentation.

Passive millimeter-wave (PMMW) is a significant potential technique for human security screening. Several popular object detection networks have been used for PMMW images. However, restricted by the low resolution and high noise of PMMW images, PMMW hidden object detection based on deep learning usually suffers from low accuracy and low classification confidence. To tackle the above problems, this paper proposes a Task-Aligned Detection Transformer network, named PMMW-DETR. In the first stage, a Denoising Coarse-to-Fine Transformer (DCFT) backbone is designed to extract long- and short-range features in the different scales. In the second stage, we propose the Query Selection module to introduce learned spatial features into the network as prior knowledge, which enhances the semantic perception capability of the network. In the third stage, aiming to improve the classification performance, we perform a Task-Aligned Dual-Head block that decouples the classification and regression tasks, and enhances the interaction between the two heads. Based on our self-developed PMMW security screening dataset, experimental results including comparison with State-Of-The-Art (SOTA) methods and ablation study demonstrate that the PMMW-DETR obtains higher accuracy and classification confidence than previous works, and exhibits robustness to the PMMW images of low quality.

Double difference bases (DDBs) and construction of thinned linear arrays with twofold redundancy are discussed in this communication. We present an analytical method, based on the combinatorial tools, to generate twofold redundancy arrays by employing regular DDB patterns separately or combining them with cyclic difference sets. Besides, the bound for DDBs is derived that would be useful to research on thinned array geometries.

Passive millimeter-wave imaging has emerged as a useful tool in many remote sensing applications, including resource re-mote sensing, material classification and target detection. This paper presents a method to classify specular objects based on their material composition from passive millimeter-wave polarimetric imagery. Passive Degree of Polarization (PDoP) is proposed and cal-culated by using the results of passive millimeter-wave polarization measurement. The PDoP values of typical ground targets are further analyzed. Outdoor experiments are conducted, and the PDoP image is generated from the brightness temperature images. The PDoP values of pixels in the image are statistically analyzed, and the threshold values are set based on the statistical analysis results, then every pixel is recognized. Experimental results indicate that this method is highly effective for distinguishing among various materials of interest.

For the near-field synthetic aperture interferometric radiometer (SAIR), the Fourier transform relationship between the visibility function and the near-field brightness temperature (BT) distribution is not valid. It is a challenging task for near-field SAIR imaging to realize very close-range accurate imaging with a large field of view (FOV). This article presents a new SAIR near-field imaging algorithm based on the angular spectrum theory to realize the passive millimeter-wave (PMMW) imaging, called synthetic-angular-spectrum imaging (SASI) algorithm. This SASI algorithm mainly addresses data samples of a 4-D visibility function acquired from planar arrays. First, we invert 4-D visibility samples into the angular spectrum domain via the Fourier transformation. The obtained angular spectrum data cannot be directly used to estimate the near-field BT distribution. Second, a dedicated phase factor compensation is adopted for handling this problem. The dimension-reducing accumulation is employed to generate the synthetic angular spectrum (SAS) of near-field BT distribution. Finally, we reconstruct the BT image by using the generated SAS data. Simulation and experiment results show that the presented SASI algorithm has the superiority on image reconstruction quality, especially for the off-axis regions, compared with the existing near-field SAIR imaging methods.