基于时域抽样法的FDTD近远场变换

提出一种基于时域抽样法的近远场变换算法以改善经典时域近远场变换算法计算量大、计算速度慢的缺点.时域抽样法是基于这样一个事实:在时域近远场变换过程中不需要与时域有限差分计算同样的时间精度.在近远场变换前先对时域近场数据进行采样以减少数据的冗余,然后用改进后的算法进行近远场变换计算从而达到减少数据量、提高计算速度的目的.为验证本算法,以计算七元八木天线远场方向图为例进行算法说明, 并与经典时域法进行比较,结果表明本算法在保证与经典法具有同样精度的前提下,减少了90%数据存储空间,同时提高计算速度80%.应用本算法可以为天线仿真优化设计、雷达散射截面(RCS)计算等提供一种快速的时域计算方法.     

基于Kirchhoff积分的瞬态声场计算方法

传统时域有限差分(FDTD)法计算远场瞬态声场特性时,通常需要将外推边界上各个时间步的场值都存储下来,占用了大量的内存,影响了计算效率.针对此问题,提出一种新的外推方法,它将柯西霍夫(Kirchhoff)公式应用于FDTD数值计算中,将积分面上每一步的近场都叠加到它有贡献的远场时刻上去,近远场变换可以与FDTD的场分量迭代同步进行,不需要将外推边界上每一时间步的场值都存储下来,减少了存储空间.针对绝对软和绝对硬的圆柱目标进行仿真计算,并与标准FDTD外推结果进行比较,结果表明:与标准FDTD的计算结果基本吻合,在节省内存量的同时不影响其计算精度.     

利用球谐方法分散计算场景的全局光照

作为一种特殊的光照模型,辐射度在图形学中有着重要的应用.然而,由于其计算的复杂性,辐射度方法一直得不到广泛的应用,特别是在动态场景中.提出一种基于辐射度算法的全局光照实时加速算法,在原有基于点采样进行场景的全局光照近似的基础上,提出了不同的点采样计算方法,以提高辐射度方法对动画场景的适应性.算法对场景中的物体建立六面体包围盒,并在包围盒各顶点设置采样点,这样就可以在环境变化对其光照影响不大的情况下采用原有的计算结果,实现辐射度算法在动态场景中的计算加速.同时,对场景中的每个物体的光照,利用立方体映射计算直接光照;而对物体间的能量交换采用点采样方法进行近似计算并利用附近采样点的光照插值作为物体的光照.实验结果表明,该算法可以减少大量的间接光照计算,提高了辐射度算法的效率.     

边界描迹算法在图像数据库的应用

本文介绍了一种优化图像数据库性能的算法－边界描迹算法,通过形态学变换减少笔迹图像的孤立点噪声,采用边界描迹的方法提取图像的基本特征,并以链码形成存储图像数据。可以大大减少图像数据的存储量,本文详细阐述了此方法的算法实现。     

CBR快速检索算法在时间序列预测中的应用

随着CBR应用的推广,涉及越来越多的时态信息需要处理.探讨了一种基于时间序列数据的时态CBR,提出了一种基于卷积的时态CBR快速检索算法.其思路是利用时序范例之间的时间约束关系,去除检索中求取相似度的冗余计算,并利用卷积的傅立叶变换性质,在频域求解相似度以减少计算时间复杂度.实验证明.在匹配较长的序列时,快速算法可以显著的提高时态CBR的检索效率.在CBR快速检索算法的基础上,以证券价格预测问题作为应用,借鉴流形学习理论中LLE算法的思想,设计了一种基于时态CBR的时间序列预测算法.实验证明,这种基于时态CBR的时间序列预测方法与前述CBR快速检索算法相配合,取得了较好的预测效果和预测效率.     

SAR原始回波数据仿真方法研究

研究SAR原始回波数据处理优化问题,处理原始回波数据,在保留回波精度的同时,还要提高计算速度.传统处理仿真方法都存在不足.为了提高SAR原始回波数据处理速度,提出一种新的时频混合仿真方法.利用时域仿真得到数据精度高的优势和频域法仿真速度快的特点,保证了回波数据的精度和速度,并将已有时频回波算法中的两次循环计算简化为一次循环计算,进一步提高仿真效率.仿真结果表明,方法可以有效地仿真SAR原始回波数据,满足系统仿真要求,为系统设计提供参考.     

基于DPP改进RANSAC算法的图像拼接

为提高图像拼接时的配准速度和精度,针对鲁棒性模型估计问题,提出一种基于行列式点过程的改进RANSAC算法（Random Sample Consensus）.该方法利用行列式点过程抽样法的全局负相关特性对匹配的特征点进行建模,实现抽样点的均匀化和分散化,剔除一些错误匹配点.用行列式点过程抽取的点集作为RANSAC算法的输入来求取变换矩阵.实验结果表明：该算法相对于传统的RANSAC算法,能够保持较高的精度和鲁棒性,减少传统RANSAC算法迭代次数,显著提升图像自动拼接的计算效率.     

多轴运动系统非线性轮廓重复跟踪的主从交叉耦合迭代学习控制

针对多轴运动系统非线性轮廓的重复跟踪,传统时域交叉耦合迭代学习控制器（Cross-coupled iterative learning control,CCILC）的设计,各轴间的耦合算子计算精度要求高,计算效率低.本文提出一种主从交叉耦合迭代学习控制方法.基于主从控制设计方法,主动轴采用时域CCILC,从动轴采用位置域交叉耦合迭代学习控制（Position domain CCILC,PDCCILC）.保证各轴间运动同步性,同时减轻对耦合算子精确性的依赖.因而可以引入轮廓误差矢量法估算耦合算子提高计算效率.采用Lifting的系统时域矩阵展开方法对所提出的算法进行了稳定性分析和性能分析.基于一个两轴毫米级运动平台,三种典型非线性轮廓跟踪（即半圆、抛物线和螺旋线）的数值仿真和实验分析验证了所提出算法的有效性.     

基于QR分解的RLS算法及在扩频系统的应用研究

相对于扩频系统抑制窄带干扰的时域经典自适应滤波算法,基于QR分解的窄带干扰抑制方法是一种性能优良的新算法.研究了基于QR分解的RLS自适应滤波算法,考虑两种指数加权递归最小二乘(RLS)估计的算法--QR-RLS算法和逆QR-RLS算法,对扩频通信系统进行窄带干扰抑制.通过定义新的数据矩阵,该算法求解出数据域正规方程中的系数矩阵和右侧向量,从而提高了计算效率.最后用Matlab仿真了自适应滤波过程,仿真结果表明,该算法具有良好的计算性能以及收敛性能.     

一种基于检索树的改进计数最近邻分类新算法

计数最近邻分类算法是一种以数据格论为理论依据的新分类算法,其优越性在于能不经转换地处理各种混合数据.本文在阐述和分析该算法的基本原理后,发现该算法的计算效率及存储效率有待改进提高,因此我们提出了一种基于检索树的改进计数最近邻分类新算法,其主要思想是通过构建检索树以减少重复数据的计算量,并以此提高算法的计算效率和存储效率.通过利用国家863项目数据集和多个UCI 公共数据集的综合测试,结果表明该新算法在具有大量重复数据的应用环境中效果明显,具有较高的计算和存储空间效率.     

Design of large-angle distortion-free and spot adjustable LED spot array projector based on microlens array and eyepiece

The spot array projector has long been a challenging and intriguing research focus in the fields of projection and lighting applications. In the current literature, there is a lack of a comprehensive and detailed presentation on the design and analysis method for a spot array generator based on the structure of imaging eyepiece and MLA. We present a novel design and optimization method for a large-angle, distortion-free and spot adjustable LED spot array projector that is composed of an eyepiece, two microlens arrays (MLAs), and a micro-LED array (MLEDA). The eyepiece system is optimized using imaging optical methods to project sub-beams to the target plane with a large angle. The sub-lens of condenser MLA is also optimized using imaging optical methods to refocus the collimating beam and match the numerical aperture (NA) with the eyepiece, and the sub-lens of the collimating MLA is acquired by using simulated annealing (SA) global illumination optics optimization method to achieve collimation and far-field homogenization. The predistortion MLEDA and the MLAs are proposed and implemented by the radial basis function (RBF) interpolation method, which correct the large-angle distortion introduced by the eyepiece. Both near-field and far-field applications can be realized by the proposed system. In the near-field applications, different spot geometries at the near-field target plane can be achieved. In the far-field applications, the power matching of the MLEDA is used to improve far-field uniformity of spot array. Moreover, the predefined-geometry arrangement spot array can be realized in both near and far fields. Two design examples with full field of view (FOV) projection of 80° and 100° are provided to validate the proposed method. Overall, the proposed system offers a promising solution for various applications requiring target identification or 3D calibration.     

水声换能器近场校准方法的实验研究

近场校准方法可用于在近场条件下校准水声换能器。方法以亥姆霍兹(Helmholtz)积分为基础,由近场测量数据推算大尺寸水声换能器的远场特性。本文叙述了三种不同类型换能器近场数据测量技术和远场特性计算方法,并作为例子给出了近远场校准结果。     

An effective localization method for mixed far-field and near-field strictly non-circular sources

In this paper, an effective direction-of-arrival (DOA) and range estimations method for mixed far-field and near-field non-circular sources is proposed based on a large centrosymmetric uniform linear array (ULA). By exploiting the non-circularity of the sources, an extended signal is generated by concatenating the received array data and its conjugate counterparts. Then the DOAs of far-field signals are estimated based on the extended covariance matrix with the traditional MUSIC algorithm. After eliminating the far-field components from the extended signal subspace, the extended covariance matrix of the near-field signals is obtained. Thus a near-field estimator is constructed based on symmetric property of the extended array manifold where the generalized ESPRIT method is adopted to estimate the DOAs of near-field sources. Finally, the range estimator is derived using the DOA estimations of near-field sources. Simulation results are provided to validate that the proposed method has achieved a better performance than existing ones and is quite suitable for massive MIMO (multiple-input multiple-out) system.     

Localization of mixed far-field and near-field sources under unknown mutual coupling

In this paper, a novel localization algorithm for mixed far-field and near-field sources is proposed in the presence of unknown mutual coupling. Based on the principle of rank reduction, direction-of-arrival (DOA) estimates of far-field sources are firstly decoupled under unknown mutual coupling. Then these estimates are employed to generate the mutual coupling coefficients. Finally, by the mutual coupling compensation and the far-field components elimination, near-field sources parameters (DOA and range) are obtained. The proposed algorithm is efficient in that it only requires second order statistics and one dimensional spectral search. Simulation results demonstrate that our algorithm is effective for the classification and localization of mixed sources under unknown mutual coupling.     

C++Builder开发的雷达天线近远场测量控制系统

本系统是用C++Builder编写的雷达天线近远场测量系统软件。本文着重介绍了系统方案、编程思想、方法、技巧等内容。     

复杂目标的近场RCS估算

为了预估复杂目标的近场雷达散射截面(RCS),通过图形电磁计算方法(GRECO)来求得复杂目标远场散射中心的雷达散射截面,采用z-buffer遮挡消隐技术来提取面元远场散射中心的位置,然后给出一种预估复杂目标近场RCS的方法.在雷达天线与目标之间的距离不是太近的情况下,通过仿真目标的远场和近场RCS的分布情况,并对其结果进行比较分析.可以证明该方法计算出来的近场RCS是有效的、准确的,可以应用到实际工程中去分析和解决问题.     

Imaging algorithm for synthetic aperture interferometric radiometer in near field

With the benefits of digital IC technology development, the synthetic aperture interferometric radiometer (SAIR) technique is growing fast and expanding to more and more application areas. The near field imaging detection is a potential application which has received increasing demand recently. Because the Fourier imaging theory of the traditional SAIR is based on far-field approximation, it will be invalid for near-field condition. This paper is devoted to establishing a new accurate imaging algorithm for ...     

不规则直流线圈磁场的计算方法研究

将不规则直流线圈等效为若干段载流直导线的组合,通过坐标变换和磁场变换计算出单根载流直导线的磁场,进而叠加求和得到整个不规则直流线圈的磁场.计算示例表明,该方法计算不规则直流线圈的磁场简单易行,可用于实际工程中线圈磁场的计算.     

A two-step approach to see-through bad weather for surveillance video quality enhancement

Adverse weather conditions such as snow, fog or heavy rain greatly reduce the visual quality of outdoor surveillance videos. Video quality enhancement can improve the visual quality of surveillance videos providing clearer images with more details to better meet human perception needs and also improve video analytics performance. Existing work in this area mainly focuses on the quality enhancement for high-resolution videos or still images, but few algorithms are developed for enhancing surveillance videos, which normally have low resolution, high noises and compression artifacts. In addition, for snow or rain conditions, the image quality of near-field view is degraded by the obscuration of apparent snowflakes or raindrops, while the quality of far-field view is degraded by the obscuration of fog-like snowflakes or raindrops. Very few video quality enhancement algorithms have been developed to handle both problems. In this paper, we propose a novel video quality enhancement algorithm for see-through snow, fog or heavy rain. Our algorithm not only improves human visual perception experiences for video surveillance, but also reveal more video contents for better video content analyses. The proposed algorithm handles both near-field and far-field snow/rain effects by proposed a two-step approach: (1) the near-field enhancement algorithm identifies obscuration pixels by snow or rain in the near-field view and removes these pixels as snowflakes or raindrops; different from state-of-the-art methods, our proposed algorithm in this step can detect snowflakes on foreground objects or background, and apply different methods to fill in the removed regions. (2) The far-field enhancement algorithm restores the image’s contrast information not only to reveal more details in the far-field view, but also to enhance the overall image’s quality; in this step, the proposed algorithm adaptively enhances the global and local contrast, which is inspired on the human visual system, and accounts for the perceptual sensitivity to noises, compression artifacts, and the texture of image content. From our extensive testing, the proposed approach significantly improves the visual quality of surveillance videos by removing snow/fog/rain effects.