Non-line-of-sight (NLOS) detection is an enduring topic as it provides a powerful tool to monitor visually blocked areas. Currently, NLOS detection requires precise prior knowledge of building layout, which limits its further applications in practice. In this article, we consider the problem of joint estimation of building layout and target location in the NLOS scenario by exploiting multipath returns. Specifically, first, the building layout is simplified into combined linear equations with unknown parameters. In this way, we establish a parametrized multipath propagation model in the multiple targets’ NLOS scenario for the multiple-input–multiple-output (MIMO) radar, which is used in the image reconstruction and layout estimation problem. Then, a shape remodeling group sparse constraint algorithm is proposed and combined with the particle swarm optimization method to simultaneously reconstruct the unknown layout and targets. Compared with the conventional compressed sensing-based methods, the proposed method integrates the basic structural characteristics and sparsity prior of the NLOS image to improve the stability of the solution. Finally, the performance of the proposed method is verified with numerical and experimental results.

This paper deals with the multiple targets localization problem via multi-channel ultra-wideband (UWB) imaging radar non-line-of-sight (NLOS) signal processing. A novel matching-based radar imaging algorithm is proposed to obtain the positions of multiple targets in the L-shaped corner scenario with complex multipath ghost signals. Firstly, a multipath propagation model for the multiple targets scenario is established. Then the positions of the actual multipath ghosts are extracted from the radar image, and the candidate targets corresponding to these multipath ghosts are derived. Secondly, the ellipse-cross-localization method is proposed to obtain the positions of the candidate multipath ghosts, followed by two defined matching factors to measure the similarity between actual and candidate multipath ghosts. According to the similarity, decision rules are designed to determine the actual targets. Compared with the localization algorithm based on one-dimensional range profile, the proposed algorithm can effectively cope with the cases of multiple targets, even in the cases of rough walls and noise. Finally, simulations and experimental data are used to validate the effectiveness of the proposed algorithm.

In automatic driving, it is difficult for automotive radar to capture the information of vehicles and pedestrians located in blind areas such as street corners and urban intersections, which may lead to collision risks. To deal with it, this paper investigates the problem of multiple target localization in a non-line-of-sight (NLOS) scenario. Specifically, first, the propagation paths of the electromagnetic waves in the L-shaped NLOS scenario are analyzed and a multipath echoes model for the multiple-input-multiple-output (MIMO) radar is established. Then, the time of arrivals (ToAs) with respect to different paths are estimated by the matrix pencil algorithm. Based on the estimated delays, a path recognition strategy, by fully considering the range and angle information of targets, is proposed to pair the ToAs with different types of multipath and targets. Finally, the performance of the proposed method is verified via simulations and experiments, which reveal that the positions of the NLOS targets can be estimated with high precision.

Non-line-of-sight (NLOS) radar has the feature of detecting the targets which are in the blind area, while the existing time-of-arrival (ToA)-based methods limit its further development in multi-target environments. To deal with it, we investigate the problem of multiple targets localization behind L-shaped corner for multiple-input-multiple-output (MIMO) millimeter wave (MMW) radar. Specifically, the multipath signal model that accounts for range, Doppler, direction of departure (DoD) and direction of arrival (DoA) is established first. After echo preprocessing, the position of the reflector wall is estimated using static point clouds. To achieve multiple NLOS targets localization with low false alarm, a multipath recognition method, by fully considering the DoD and DoA relationships of multipaths, is proposed. Furthermore, the real target positions are derived by mirror symmetry. Finally, the performance of the method is tested by the experiments, which reveal that the proposed method can work in the presence of multiple NLOS targets with high accuracy and low false alarm.

This letter considers the problem of the target detection in the nonhomogeneous sea clutter environment, and proposes the complex-valued U-Net based clutter suppression method. Specifically, firstly, the complex signal features of radar echo sequences are extracted by developing the complex-valued convolutional blocks. Secondly, the complex multi-level features are fused, by employing the up-down sampling structure and skip connections, to suppress nonhomogeneous sea clutter. Further, the false alarm controllable detector is designed to detect the targets. Finally, the performance of the proposed method is evaluated via real data. The results show that it has a higher detection probability compared with the real-valued U-Net.

Localization of non-line-of-sight (NLOS) targets in the complex urban environment has attracted significant attention in recent years. However, the requirement for precise prior information about the environment is idealistic. It is challenging to know the environmental information in the blind area of vision in advance of practical applications. This article proposes a joint estimation algorithm for building layout and target position in the L-shaped scene without any prior information. Specifically, a round-trip multipath propagation model is first developed for the cases of diffraction and multiple reflections. Then, the received echo signal is preprocessed with moving target identification (MTI), back-projection (BP) imaging, and image segmentation. In addition, the target points, which are screened by the geometric association, are further matched and estimated by the multipath ghost’s hypothesis method, thus realizing the joint perceptual estimation of the building layout and the target position. Finally, electromagnetic (EM) simulations and experimental measurements are used to validate the effectiveness of the proposed algorithm.