Non-line-of-sight (NLOS) imaging has emerged as a prominent technique for reconstructing obscured objects from images that undergo multiple diffuse reflections. This imaging method has garnered significant attention in diverse domains, including remote sensing, rescue operations, and intelligent driving, due to its wide-ranging potential applications. Nevertheless, accurately modeling the incident light direction, which carries energy and is captured by the detector amidst random diffuse reflection directions, poses a considerable challenge. This challenge hinders the acquisition of precise forward and inverse physical models for NLOS imaging, which are crucial for achieving high-quality reconstructions. In this study, we propose a point spread function (PSF) model for the NLOS imaging system utilizing ray tracing with random angles. Furthermore, we introduce a reconstruction method, termed the physics-constrained inverse network (PCIN), which establishes an accurate PSF model and inverse physical model by leveraging the interplay between PSF constraints and the optimization of a convolutional neural network. The PCIN approach initializes the parameters randomly, guided by the constraints of the forward PSF model, thereby obviating the need for extensive training data sets, as required by traditional deep-learning methods. Through alternating iteration and gradient descent algorithms, we iteratively optimize the diffuse reflection angles in the PSF model and the neural network parameters. The results demonstrate that PCIN achieves efficient data utilization by not necessitating a large number of actual ground data groups. Moreover, the experimental findings confirm that the proposed method effectively restores the hidden object features with high accuracy.

In this Letter, we propose and experimentally demonstrate a lens-free wavefront shaping method that utilizes synchronized signal block beam alignment and a genetic algorithm (SSBGA) for a diffuse non-line-of-sight (NLOS) visible light communication (VLC) system. The proposed method effectively controls the position and mobility of visible light beams by partitioning spatial light modulator pixels and manipulating beams to converge at distinct spatial positions, thereby enhancing wavefront shaping efficiency, which achieves a significant 23.9 dB optical power enhancement at +2mm offset, surpassing the lens-based continuous sequence (CS) scheme by 21.7 dB. At +40° angle, the improvement reaches up to 11.8 dB and 16.8 dB compared to the results with and without lens-based CS, respectively. A maximum rate of 5.16 Gbps is successfully achieved using bit-power loading discrete multi-tone (DMT) modulation and the proposed SSBGA in an NLOS VLC system, which outperforms the lens-based CS by 1.07 Gbps and obtains a power saving of 55.6% during the transmission at 4 Gbps. To the best of our knowledge, this is the first time that high-speed communication has been realized in an NLOS VLC system without a lens.

随着智能技术和设备的不断发展，精确定位技术在**领域的应用越来越广泛，其应用场景涵盖室外和室内环境。全球卫星导航系统定位技术在室外环境中定位精度高，提供信息丰富，应用十分普遍。然而，由于墙壁、树木、玻璃等障碍物的遮挡，其在室内环境中的定位精度明显下降。超宽带技术以其定位精度高、时空分辨率强、传输速率快、成本低而显示出明显的优势。在室内环境中，各种障碍物使超宽带系统的基站和标签之间的传播通道被阻挡，由于超宽带信号的非视距传播现象，超宽带系统的定位精度明显下降。文中基于深度学习技术，提出了一种深度神经网络用于超宽带非视距传播影响抑制，该深度神经网络兼具ResNet网络和Non-local模块的优点，既可防止网络层数增加时网络性能不升反降的问题，也可捕获输入数据的全局特征，建立超宽带系统原始信道脉冲响应和测距误差之间的映射关系。相关实验结果显示，该方法可将超宽带系统在非视距传播条件下的测距平均绝对误差从0.1242 m降低至0.0681 m。与传统方法相比，该方法可消除人工统计超宽带信号波形特征耗费大量时间的缺点，可进一步提高超宽带系统在非视距传播条件下的定位精度，具有鲁棒性强、应用范围广的优点，可为**领域室内高精度定位提供技术支撑。

非视域（Non-Line-of-Sight, NLoS）成像是近年来发展起来的一项新兴技术，其通过分析成像场景中的中介面信息来重建隐藏场景，实现了“拐弯成像”的效果，在多个领域有巨大的应用价值。本文主要针对NLoS成像重建算法进行综述性研究。考虑到目前NLoS成像分类存在交叉和非独立现象，本文基于物理成像模式和算法模型的不同特点，对其进行了独立的重新分类。根据提出的分类标准分别对传统和基于深度学习的NLoS成像重建算法进行了归纳总结，对代表性算法的发展现状进行了概述，推导了典型方法的实现原理，并对比了传统重建方法和基于深度学习的NLoS成像重建算法的重建应用结果。总结了NLoS成像目前存在的挑战和未来的发展方向。该研究对不同类型的NLoS成像进行了较为全面的梳理，对NLoS成像重建算法在内的一系列研究的进一步发展有着一定的支撑和推动作用。

Non-line-of-sight (NLOS) imaging is an emerging technique for detecting objects behind obstacles or around corners. Recent studies on passive NLOS mainly focus on steady-state measurement and reconstruction methods, which show limitations in recognition of moving targets. To the best of our knowledge, we propose a novel event-based passive NLOS imaging method. We acquire asynchronous event-based data of the diffusion spot on the relay surface, which contains detailed dynamic information of the NLOS target, and efficiently ease the degradation caused by target movement. In addition, we demonstrate the event-based cues based on the derivation of an event-NLOS forward model. Furthermore, we propose the first event-based NLOS imaging data set, EM-NLOS, and the movement feature is extracted by time-surface representation. We compare the reconstructions through event-based data with frame-based data. The event-based method performs well on peak signal-to-noise ratio and learned perceptual image patch similarity, which is 20% and 10% better than the frame-based method.