The detection of objects concealed under people’s clothing is a very challenging task, which has crucial applications for security. When testing the human body for metal contraband, the concealed targets are usually small in size and are required to be detected within a few seconds. Focusing on weapon detection, this paper proposes using a real-time detection method for detecting concealed metallic weapons on the human body applied to passive millimeter wave (PMMW) imagery based on the You Only Look Once (YOLO) algorithm, YOLOv3, and a small sample dataset. The experimental results from YOLOv3-13, YOLOv3-53, and Single Shot MultiBox Detector (SSD) algorithm, SSD-VGG16, are compared ultimately, using the same PMMW dataset. For the perspective of detection accuracy, detection speed, and computation resource, it shows that the YOLOv3-53 model had a detection speed of 36 frames per second (FPS) and a mean average precision (mAP) of 95% on a GPU-1080Ti computer, more effective and feasible for the real-time detection of weapon contraband on human body for PMMW images, even with small sample data.

Tomographic Synthetic Aperture Radar (TomoSAR) has three-dimensional imaging capability. Nevertheless, its building structure is full of considerable noise and wrong targets owing to the baseline distribution and the algorithm restrictions. Accordingly, efficiently extracting and segmenting buildings from SAR point clouds with huge data is a critical issue. According to the characteristics of building facade point clouds, the KD-Tree-based Euclidean clustering is introduced as a fast-clustering method for better extracting and segmenting individual building facades. At first, a simple morphological filter is utilized to identify the ground and non-ground point clouds. The building facades are then extracted from the non-ground point clouds based on density information. Subsequently, the KD-Tree-based Euclidean clustering method denoises the building facades and segments them into individual facades. Finally, a second Euclidean clustering is performed for each of these facades to denoise them again. Experimental results indicate that the presented approach can prepare accurate and efficient extraction and segmentation results for complex urban scenes.

Tomographic synthetic aperture radar (TomoSAR) has been widely used in three-dimensional (3D) reconstruction of urban buildings. However, due to the baseline distribution and the limitations of the algorithm itself, the building point cloud after tomographic imaging is flooded by substantial noise and/or false targets. Thus, TomoSAR point clouds must be extracted from these unwanted factors to reconstruct the building structure. Existing line-based extraction methods can only detect straight lines, which results in the loss of non-linear point clouds. Thus, inspired by density clustering, we propose a point cloud extraction method using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. The DBSCAN can preserve the building structure more completely by enabling the extraction of various shapes of the buildings. Since the detection of point clouds is density-based, noise and false targets that exhibit low-density distribution can be accurately detected and rejected. The experimental results demonstrated the effectiveness of our method for TomoSAR point cloud extraction, as well as the structural protection of buildings, which achieves a higher extraction accuracy compared to linear detection.

Bistatic radar possesses the forward-looking imaging performance that conventional synthetic aperture radar (SAR) lacks. Nevertheless, bistatic forward-looking imaging is seriously limited by the motion of the transmitter and receiver, resulting in a restrained resolution. In this work, the method for vortex missile-borne bistatic synthetic aperture radar (BSAR) is proposed, which combines the orbital angular momentum (OAM) beam with the traditional missile-borne BSAR technique to obtain a larger Doppler bandwidth. Higher azimuth resolution is achieved by compensating large spatial range cell migration (RCM) and serious range-azimuth coupling. Firstly, the imaging scene of vortex missile-borne BSAR and the OAM-based echo model are established and deduced. Secondly, the preprocessing method for amplitude compensation is proposed with the design of the transmitted OAM beam, which improves the uncorrelation between different pulse echoes. Then, the improved range Doppler (RD) algorithm with azimuth compensation factor is proposed for vortex missile-borne BSAR to obtain the super-resolution two-dimensional (2D) reconstruction for the target. Simulation results show that compared with conventional missile-borne BSAR, the proposed vortex missile-borne BSAR possessing the extending Doppler bandwidth can obtain superior azimuth resolution performance with the identical synthetic aperture length situation. Furthermore, this work will contribute to the application for OAM-carrying in the field of radar forward-looking imaging and the development for high-resolution missile-borne BSAR imaging technology.

Tomographic synthetic aperture radar (TomoSAR) produces 3-D point clouds with unavoidable noise or false targets that seriously deteriorate the quality of 3-D images and the building reconstruction over urban areas. In this paper, a Hough transform was adopted to detect the outline of a building; however, on one hand, the obtained outline of a building with Hough transform is broken, and on the other hand, some of these broken lines belong to the same segment of a building outline, but the parameters of these lines are slightly different. These problems will lead to that segment of a building outline being represented by multiple different parameters in the Hough transform. Therefore, an unsupervised clustering method was employed for clustering these line parameters. The lines gathered in the same cluster were considered to correspond to a same segment of a building outline. In this way, different line parameters corresponding to a segment of a building outline were integrated into one and then the continuous outline of the building in cloud points was obtained. Steps of the proposed data processing method were as follows. First, the Hough transform was made use of to detect the lines on the tomography plane in TomoSAR point clouds. These detected lines lay on the outline of the building, but they were broken due to the density variation of point clouds. Second, the lines detected using the Hough transform were grouped as a date set for training the building outline. Unsupervised clustering was utilized to classify the lines in several clusters. The cluster number was automatically determined via the unsupervised clustering algorithm, which meant the number of straight segments of the building edge was obtained. The lines in each cluster were considered to belong to the same straight segment of the building outline. Then, within each cluster, which represents a part or a segment of the building edge, a repaired straight line was constructed. Third, between each two clusters or each two segments of the building outline, the joint point was estimated by extending the two segments. Therefore, the building outline was obtained as completely as possible. Finally, taking the estimated building outline as the clustering center, supervised learning algorithm was used to classify the building cloud point and the noise (or false targets), then the building cloud point was refined. Then, our refined and unrefined data were fed into the neural network for building the 3-D construction. The comparison results show the correctness and the effectiveness of our improved method.