基于欺骗式动目标的SAR干扰技术研究

针对军事运动目标的保护问题，提出了基于欺骗式动目标的SAR干扰方法，该方法根据收到的雷达信号，调制不同的散射强度和位置信息，模拟产生多个虚假的运动目标，避免了静止场景不能干扰动目标成像的缺点。分析了雷达位置确定误差对欺骗式动目标干扰效果的影响，给出了一种干扰机的布阵方法。仿真证明，欺骗式动目标干扰方法可以达到保护运动目标和向被干扰雷达提供虚假信息的目的。     

太空垃圾实时监测系统建立可行性分析

介绍了太空垃圾监测及清除工作现状,分析了现有监测太空垃圾的雷达技术、激光技术及激光雷达技术的可行性,以STK软件模拟分析太空垃圾运动轨迹,得出了太空垃圾实时监测系统的可实现方法和建议。     

Chinese Surveying and Control Network for Earth-Orbit Satellites and Deep Space Detection

Introduction Astorealizetheprojectofspatialgeodesyby geodeticsatellites,itisnecessarytoestablisha satellitecontrollingandsurveyingnetwork(SC SN)totrack,tosurvey,toremotelycontroland tocommunicatewithearth orbitsatellite,forre alizingtheprojectofdeepspaced…     

偏振激光雷达探测大气—水体光学参数廓线

激光雷达在上层水体垂直廓线的遥感中展现出巨大优势。本文研制了一套高垂直分辨率的实时探测偏振激光雷达,提出了一种基于偏振激光雷达回波信号的反演算法,采用Fernald理论和多次散射原理反演非均匀大气—水体的衰减和退偏光学产品,以高效稳定地处理偏振激光雷达实验数据。展示了一个中国内陆水体激光雷达探测实例,观测到了两次气溶胶积聚现象和一次水体浑浊现象。对实验数据的分析表明,退偏比主要由前向多次散射和后向单次散射产生的退偏两部分组成。当多次散射强度较大时,退偏比的变化主要取决于多次前向散射退偏;反之,则主要依赖于单次后向散射退偏。     

雷达遥感六十年：四个阶段的发展

雷达遥感问世60年来已经历了4个阶段的发展,其在对地观测中的作用正日益凸显,已经广泛应用于不同领域。4个阶段分别是单波段单极化阶段,多波段多极化阶段,极化和干涉阶段,以及以双/多站或星座、高时序高分宽幅、3维成像为代表的新阶段。本文结合作者长期在雷达遥感领域的研究经历,总结和回顾了雷达遥感的阶段发展和具有里程碑式的代表性技术;从观测技术、数据处理和应用角度阐述了新阶段雷达遥感的发展趋势,以及雷达遥感与人工智能和大数据结合的思考;最后着眼未来,介绍了月基雷达对地观测平台的前瞻性研究。     

An improved building detection approach using L-band POLSAR two-dimensional time-frequency decomposition over oriented built-up areas

The objective of this study is to efficiently extract detailed information about various man-made targets in oriented built-up areas using polarimetric synthetic aperture radar (POLSAR) images. This paper develops an improved approach for building detection by utilizing Two-Dimensional Time-Frequency (2-D TF) decomposition. This method performs outstandingly in distinguishing between man-made and natural targets based on the isotropic behaviors, frequency-sensitive responses, and scattering mechanisms of objects. The proposed method can preserve the spatial resolution and exploit the advantages of TF decomposition; specifically, the exact outlines of buildings can be effectively located, and more types of features (e.g., flat roofs, roads, and walls that are oblique to the radar illumination) can be distinguished from forests in complex built-up areas by 2-D TF decomposition. The coarser-resolution subaperture images that are produced in the azimuth direction, which correspond to different looking angles, are beneficial for detecting man-made structures with main scattering centers oriented at oblique angles with respect to the radar illumination. In the range direction, the obtained subaperture images, which correspond to various observation frequencies, can be helpful in distinguishing flat roofs and roads from forests. This method was successfully implemented to analyze both NASA/JPL L-band AIRSAR and L-band EMISAR data sets. The building detection results of the proposed method exhibit a significant improvement over those of other methods and reach an overall accuracy over 80%, with approximately 20% higher than the accuracies of K-means clustering and the entropy/alpha-Wishart classifier and approximately 10% higher than the accuracy of the support vector machine method. Moreover, building details can be precisely detected, obliquely oriented buildings can be identified, and the distinction between buildings and forests is significantly improved, as both visually and statistically indicated. This method is highly adaptable and has substantial application value.     

SAR image despeckling with a multilayer perceptron neural network

Speckle noise in synthetic-aperture radar (SAR) images severely hinders remote sensing applications; therefore, the appropriate removal of speckle noise is crucial. This paper elaborates on the multilayer perceptron (MLP) neural-network model for SAR image despeckling by using a time series of SAR images. Unlike other filtering methods that use only a single radar intensity image to derive their parameters and filter that single image, this method can be trained using archived images over an area of interest to self-learn the intensity characteristics of image patches and then adaptively determine the weights and thresholds by using a neural network for image despeckling. Several hidden layers are designed for feedforward network training, and back-propagation stochastic gradient descent is adopted to reduce the error between the target output and neural-network output. The parameters in the network are automatically updated in the training process. The greatest advantage of MLP is that once the despeckling parameters are determined, they can be used to process not only new images in the same area but also images in completely different locations. Tests with images from TerraSAR-X in selected areas indicated that MLP shows satisfactory performance with respect to noise reduction and edge preservation. The overall image quality obtained using MLP was markedly higher than that obtained using numerous other filters. In comparison with other recently developed filters, this method yields a slightly higher image quality, and it demonstrates the powerful capabilities of computer learning using SAR images, which indicate the promising prospect of applying MLP to SAR image despeckling.     

平原地区机载激光雷达数据的抽稀算法分析

目前,机载激光雷达点云数据在测绘行业中的应用还存在较多的瓶颈。为了使机载激光雷达点云数据更好地服务等值线等数据的生产,发挥其高效和高精度的优势,本文归纳、总结了国内外现有的LiDAR点云数据抽稀算法,并通过对比分析现有LiDAR点云数据抽稀算法存在的优缺点,如系统抽稀、格网抽稀、TIN抽稀和坡度抽稀等算法,结合平原地区激光点云在实际生产中的应用,研究了更适合平原地区点云数据的抽稀方法,通过大量的数据测试和试生产。结果表明,该方法可以在应用项目精度约束下保证数据质量,减少了后期数据处理应用的难度,提升了后续成果数据的质量,提高了作业生产效率,对机载激光雷达点云数据在测绘行业中的应用推广具有重要的现实意义。     

单目视觉下基于对偶四元数的运动目标位姿确定

利用对偶四元数研究运动载体和目标之间相对运动的单目算法大多是仿真验证,缺乏实验验证。针对这些不足,在James方法的基础上,扩展了状态量,推导了相应的系统模型和观测模型,组成新的滤波方程。匀速旋转的转台实验验证了改进算法的优越性。     

基于极化合成理论的SAR目标最优极化研究

基于Stokes矩阵以及极化合成原理推导出一种解算目标最优接收功率的新算法。该算法充分利用了Stokes矩阵及范数原理,在计算目标最优接收功率上的精度优于传统算法,计算过程比传统算法简单、直接,且易于实现,不仅适用于完全极化波,而且适用于部分极化波和完全非极化波。实验证明,该算法可得到理想的结果。