基于阵列流形分离的多输入多输出信道模型

基于三维阵列流形分离技术,建立了一种天线独立的随机多输入多输出(MIMO)信道模型。将天线阵列的导向矢量进行球谐基函数展开,实现了天线阵列和无线传播环境的分离。对分离出的无线传播环境部分单独进行基于空间相关性的统计建模,得到了一种天线独立的解析MIMO信道模型。所建模型可方便用于MIMO系统性能评估与分析设计。以圆柱阵列和球阵列为例,仿真验证了新模型的正确性和有效性。     

Multilinear array manifold interpolation

Two algorithmic solutions for interpolating between array manifold grid points are presented. The algorithms are for sensor arrays in vector or in scalar wavefields. The algorithms make successful use of the condition that the reciprocal DOA (direction-of-arrival) spectrum is a multilinear function in the small, i.e. over the region of adjacent grid points. Since polarization is a linear parameter subspace, a DOA spectrum can be computed without recourse to the unknown polarizations. Then, vector interpolation is replaced by bivector interpolation to address the radio wavefield case. The approach distinguishes between sensor arrays in scalar wavefields (e.g. acoustic) and those in vector wavefields (e.g. electromagnetic). Since both storage and computational load vary with the reciprocal of the grid spacing or its square, the design relationship between desired accuracy and the required array manifold grid spacing is given     

The impact of sensor positioning on the array manifold

A framework is presented in this study which analyzes the importance of each sensor in an antenna array based on its location in the overall geometry. Moving away from the conventional methods of measuring sensor importance by means of application-specific criteria, a new approach, based on the array manifold and its differential geometry, is used to measure the significance of each sensor's position in an array. By considering two-dimensional manifolds (function of azimuth and elevation angles) of planar arrays of isotropic sensors, and by using previously established results on the differential geometry of such manifolds, a sensitivity analysis is performed which leads to characterization and quantification of sensor location importance in an array geometry. Based on this framework, a criterion is developed for assessing an overall geometry. This criterion may be used as a figure of merit for comparing diverse array geometries.     

Forward-backward averaging in the presence of array manifold errors

We investigate the use of forward-backward (f/b) averaging for estimating the covariance matrix used for adaptive beamforming and space-time adaptive processing (STAP). We demonstrate that the estimation loss is reduced by the use of f/b averaging and, for some STAP cases, f/b averaging can even quadruple the available sample support. We also show that unknown array manifold errors have little effect on the effectiveness of f/b averaging. The gain from f/b averaging is demonstrated on data from the mountaintop database     

基于一种改进型阵列结构联合估计二维方向角

针对一种基于ESPRIT的算法进行二维DOA估计时，在一定条件下会出现角度估计模糊的现象，分析指出了其中方法性能缺陷的理论原因，优化了阵列结构，在此基础上对矩阵束求解方法进行改进，针对类L型阵列结构采用改进算法能有效解决DOA联合估计中的角度配对问题，实现了信号方向参数的自动配对。仿真实验证实本文提出的改进算法的有效性。     

一种基于等效间距法的低散射阵列设计

针对阵列天线在某些角域出现强散射的现象,提出了一种等效间距法并分析了强散射点的产生机理,给出了抑制散射峰值同时满足辐射要求的最佳折中阵列间距确定准则。运用该方法对8单元贴片阵列进行了实例验证,结果表明:设计的贴片阵列可以在满足辐射性能的基础上,带内和带外单站雷达散射截面的强散射点得到了有效抑制,使其在绝大多数入射角处于低散射状态,从而证明了该方法可以快速、有效地指导兼顾辐射性能的低散射阵列设计。     

Wide angle space laser communication receiver based on asymmetric array structure

In this paper, an asymmetric array structure of space laser communication receiver is proposed. This structure can greatly reduce alignment requirement, and lighten the signal strength jitter caused by atmospheric turbulence. A prototype of the proposed structure is fabricated and a 2.5 Mbit/s on-off keying (OOK) modulated demonstration link over 40 m free space is built. This asymmetric array structure can effectively collect optical signal while rotating in a window angle of ±17°, and the bit error ratio (BER) keeps zero.     

基于盲源分离的共形阵列二维DOA估计

本文给出了一种基于特征矩阵联合近似对角化（JADE, Joint Approximate Diagonalization of Eigen-matrices）的共形阵列信号DOA估计算法。该算法首先对接收数据进行白化处理,利用白化后的数据构造四阶累计量矩阵,通过对特征矩阵联合近似对角化获得流形矢量矩阵的估计,从而实现信号的DOA估计。算法对阵列形状限制小,无需谱峰搜索。以圆台共形阵列为例,通过仿真实验验证了算法的有效性。     

球源空间均匀分布阵的空间辐射场特性研究

定义了球源空间均匀分布阵的概念，从理论上推导了球源空间均匀分布阵远场声压和指向性公式，分析了它们与场点的位置、球源个数、相邻球源中心间距、波数及球源表面振速等因素的关系。并且对它们的关系作了仿真实验。在理论与仿真分析的基础上，给出了球源阵的设计原则：(1)根据球源空间阵大小的实际需要，确定球源个数和布阵方式；(2)球源本身的半径或体积尽可能选小一些，这样可减小阵的体积，以便尽可能增加球源个数并增强指向性；(3)在球源半径一定的条件下，相邻球源中心间距应尽量减小，最小到为球源直径的两倍。     

Compact versatile thermooptical space switch based on beam steeringby a waveguide array

We present the implementation of an optical space switching concept based on thermooptic beam steering in an arrayed waveguide grating. Individual addressing of heaters on the array waveguides enables flexible switching of input signals to output waveguide channels. The switch can be used either as a 1-to-8 switch or as multiple parallel 1-to-4, 1-to-3, or 1-to-2 switches. For the implementation we used high-refractive-index SiON-SiO₂ planar waveguide technology, which allows the realization of compact devices using small bending radii. We achieved isolations of the “off'” channels with respect to the “on” channel of up to 29 dB for a 1-to-3 switch or better than 22 dB for all “off” waveguides for a 1-to-8 switch     

导航卫星阵列天线的快速现场系统校准算法

阵列天线接收处理全球卫星导航信号时,由于天线和射频前端的非理想性,会在不同方向卫星信号中引入不同的载波相位偏差,从而破坏卫星导航接收机距离观测量准确性。针对此问题,提出了一种易实现的卫星导航阵列天线的快速现场系统校准算法。校准过程分为通道校准和天线校准：通过开机和周期进行的通道校准实现阵列天线接收射频前端的通道响应测量;通过与接收处理不同卫星基带数据的数字接收机配合,实现阵列天线的现场阵列流形矢量校准。仿真验证了所提校准算法的正确性和有效性。     

空间遥感器线阵与面阵探测器共基板焦面组件设计

空间遥感器的焦面组件是空间遥感器的一个关键部组件,完成光信号到电信号的转变功能,要求其具有高拼接精度、高稳定性和高度轻量化的性能。针对某深空探测遥感器兼具推扫成像和凝视成像的功能需求,设计了一种线阵面阵探测器共基板的焦面组件,主要包括焦面基板、CCD组件、CMOS组件和视频处理电路板等。通过合理的布局,在一块焦面基板上完成了三片CCD芯片和两片CMOS芯片的布局设计,结构紧凑,散热效果好,同时利用铜片分割和电箱结构覆盖的手段提升了焦面组件的电测兼容性能。完成焦面组件的拼接,CCD探测器的搭接误差优于±2 μm,各个探测器的共线和平行度误差优于±2 μm,共面度误差优于±2.5 μm。最终,对焦面组件开展了力、热环境试验,探测器的共线、平行度和共面度误差均无变化,表明焦面组件具有足够高的拼接精度和稳定性,焦面组件的基频为135 Hz,具有足够高的动态刚度,同时,还对整机进行了电磁兼容试验,性能良好。     

Intrinsic dimensionality estimation based on manifold assumption

Dimensionality reduction is an important tool and has been widely used in many fields of data mining and machine learning. Intrinsic dimension of data sets is a key parameter for dimensionality reduction. In this paper, a new intrinsic dimension estimation method based on geometrical relationship between manifold intrinsic dimension and data neighborhood geodesic distances is presented. The estimator is derived by manifold sampling assumption. On a densely sampled manifold, the number of samples that fall into a ball is equal to the volume times the density of the ball. The radius of the ball is calculated by graph distance which is approximation of geodesic distance on manifold. Then the intrinsic dimension is estimated on each sample. Experiments conducted on synthetic and real world data set show that the performance of our new method is robust and comparable to other works.     

基于流形学习的混合光谱解混分析

光谱解混分析的重要研究内容是计算分析各地物类别成分在混合像素内所占的比例技术。文中以实测高光谱数据为研究对象,针对高光谱数据具有高维度数、严重的光谱混合等特点,基于流形学习中局部线性嵌入（LLE）算法的思想,提出了一种约束最小乘方局部线性加权回归（CLS-LLWR）建模方法。通过4种典型地物的光谱吸收特征差异分析,从它们不同比例组合下的实测混合光谱中选取了不同波段范围,分别对该模型预测覆盖度信息能力进行了验证分析。最后,将CLS-LLWR模型与主成分回归（PCR）和偏最小二乘回归（PLSR）模型,通过计算预测标准误差（SE）进行了对比分析。结果表明,CLS-LLWR模型有较好的预测能力。这为流形学习在高光谱遥感图像信息提取方面进行了有意的探索。     

阵列天线排阵间距与天线增益的关系探讨

在天线的设计中，为增强天线的方向性、提高天线增益，经常用到天线阵列。阵列天线的增益与排阵间距有关。对于具有某一波束宽度的天线，给出了能使阵列天线获得最大增益的最佳间距。在工程设计应用上，分析了高增益单元天线进行排阵时排阵间距选择问题。     

基于流形等距映射的矩阵信息几何检测方法

雷达目标检测常面临复杂的杂波特性,经典的检测方法通常适合于某些特定的场景,当检测背景发生变化时,其检测性能急剧下降。为有效提升不同杂波背景下的检测性能,提出一种基于流形等距映射(ISOMAP)的矩阵信息几何检测器。该方法首先将信号检测问题转化为矩阵流形上两点之间的区分性问题;然后基于样本数据和流形等距映射原理,自适应地学习出矩阵流形的投影变换矩阵,将矩阵流形变换为可区分的低维流形,最大程度地保持每一个矩阵与其邻域内矩阵之间几何距离大小,增强矩阵流形的可分性;最后利用仿真杂波和实测数据对算法进行验证。实验结果表明,相比于经典的检测方法,所提方法能有效提升目标检测性能。     

基于流形波段选择的高光谱图像分类

为解决高光谱图像中高维数据和有标记训练样本不 足的矛盾导致“维度灾难”问题,提出一种无监督的基于流形学习的波段选择(MLBS)方法。 首先通过流形学习方法,得到原始数据的流形嵌入映射;然后通过LASSO优化过程,运用顺 向坐标下降算法,得到原始波段对每个流形结构 维度的贡献度;最后统计每个波段的贡献度,选取贡献度大的波段形成波段子集。用 真实的AVIRIS高光谱图像对算法进行仿真实验的结果表明,本文方法在小样本下的高光谱 地物分类识别问题上具有良好的效果。     

基于协方差流形的异常驾驶行为识别方法

研究一种高效的异常驾驶行为正确识别分类的识别方法,对预防由于异常驾驶行为导致的交通事故具有重要意义。提出了一种新的基于协方差流形的异常驾驶行为识别方法。首先提取图像的纹理、颜色和梯度方向特征,以克服基于单一特征识别驾驶行为的不足;并利用协方差流形进行多特征融合,以消除特征冗余以及不同特征数值悬殊对图像识别的影响;最后使用多类LogitBoost分类器进行分类识别。针对相同检测目标的正确识别率可达98%以上,对不同检测目标的正确识别率可达70%以上。实验结果表明该方法有效提高了驾驶行为识别的效果。     

Weakly supervised learning based on hypergraph manifold ranking

Significant challenges still remain despite the impressive recent advances in machine learning techniques, particularly in multimedia data understanding. One of the main challenges in real-world scenarios is the nature and relation between training and test datasets. Very often, only small sets of coarse-grained labeled data are available to train models, which are expected to be applied on large datasets and fine-grained tasks. Weakly supervised learning approaches handle such constraints by maximizing useful training information in labeled and unlabeled data. In this research direction, we propose a weakly supervised approach that analyzes the dataset manifold to expand the available labeled set. A hypergraph manifold ranking algorithm is exploited to represent the contextual similarity information encoded in the unlabeled data and identify strong similarity relations, which are taken as a path to label expansion. The expanded labeled set is subsequently exploited for a more comprehensive and accurate training process. The proposed model was evaluated jointly with supervised and semi-supervised classifiers, including Graph Convolutional Networks. The experimental results on image and video datasets demonstrate significant gains and accurate results for different classifiers in diverse scenarios.     

大气扰动对深空通信天线组阵性能的影响

利用单站GNSS精密单点定位原理(Precise Point Positioning, PPP)解算天顶方向的对流层大气天顶延迟, 并对其一天内的变化进行统计平均, 结合经典的Kolmogorov-Obukhov“2/3”扰动理论和随机场理论, 基于对流层天顶延迟与大气折射率的时变相关性获得大气结构常数C_n, 并分析了不同大气扰动强度下的二元天线阵大气相位误差.通过计算我国北京、上海、昆明、乌鲁木齐四个甚长基线干涉测量(Very Long Baseline Interferometry, VLBI)深空站处不同时间的C_n2, 评估各站点大气环境的扰动特性对X/Ka频段天线阵系统工作性能的影响, 统计结果表明:除了北京、乌鲁木齐站在1月份的相位误差相对较小外, 各站其他时间在Ka频段的相位误差大多数达到25°以上, 北京的7月和上海的4月甚至高达50°以上, 会严重降低天线阵信噪比合成性能, 影响地面站与深空探测器之间的大容量通信.