目标的变化和任务的拓展对光电跟瞄系统提出了快速机动的要求，从地基平台到车载、船载、机载、星载等运动平台是光电跟瞄系统的重要发展趋势。基于惯性参考单元（Inertial Reference Unit，IRU）的视轴稳定方式是克服运动平台高频扰动，实现光电跟瞄系统微弧度甚至亚微弧度级跟瞄的主要技术手段。针对运动平台光电跟瞄系统精确指向对载体基座扰动抑制的需求，分析和对比了IRU的各种技术方案，特别介绍了利用低噪声、宽频带惯性传感器敏感角扰动，并通过反馈控制实现视轴惯性稳定的系统方案。从此类IRU系统的工作原理出发，阐述了系统的两种工作模式及功能特点，建立了系统数学模型。然后，介绍了IRU的国内外研究进展及发展方向，指出惯性传感、支承结构和控制系统是决定IRU稳定能力的关键因素，梳理了三项关键技术的研究动态。最后，总结了IRU的空间应用情况，并结合目前的应用需求对其未来应用领域进行了探讨。

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。

随着用户对定位精度的要求越来越高，遥感器对视轴稳定性的要求指标也越来越高。随着相机系统复杂性的提高，部组件设计完毕后复算视轴稳定性的迭代设计方法越来越不可取。为将视轴稳定性指标分解到部组件，利用线性光学理论，以某相机为例，使用CODEV得到光学系统的视轴灵敏度矩阵。在此基础上，使用蒙特卡洛法将相机总体视轴稳定性指标分解到了各部组件。结果表明，若要视轴保持0.45″（CE90）的稳定性指标，主镜、次镜、三镜应分别保证最大平移不超过0.76、1.5、2.5 μm，最大角位移不超过0.1″、0.4″、0.8″。最后，根据设计后的相机模型复算了相机视轴稳定性，结果表明满足总体指标。该方法可为复杂遥感器设计之初的视轴稳定性指标分解提供参考。

本文设计了一种可以使光电伺服平台对目标对象进行高精度、稳定追踪的基于双速度环的扰动观测器，可以消除光电平台内部摩擦力矩、外部载体扰动以及传感器噪声的影响，提升系统的动态响应性能。首先，根据直流电机工作原理与负载模型，建立双速度环的数学控制模型。接着，通过分析多类型传感器的速度信号频谱和响应性能，选择噪声和延时较小的圆光栅代替传统测速设备，作为速度控制内环；同时选择光纤陀螺作为速度外环的反馈设备。然后，基于陀螺速度信号设计扰动观测器，对内速度环中的扰动补偿残差和外部载体扰动信号进行观测，并进行前馈信号补偿。实验结果表明，双速度环观测器的控制方法可以将系统调节时间降至原来的45%，在不同幅值（0.25°~2°）和频率（0.25 Hz~2 Hz）的正弦扰动信号下，该方法均能显著提高系统的扰动抑制能力，并将系统隔离度由原来的20.9 dB提升至30 dB。本文所提出的基于双速度环扰动观测器的控制方法满足光电跟踪平台快速响应、跟踪稳定、抗干扰能力强等要求。

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.