用MoM-UTD混合方法分析机载平台天线间的隔离度

天线间的隔离度是机载平台电磁兼容(EMC)的重要指标之一。由于机载平台的电尺寸很大,因此单纯矩量法(MoM)很难解决。一致性几何绕射理论(UTD)虽是高频方法,适合解决电大问题,但其精度较低。该文采用MoM-UTD混合方法分析机载平台天线间的隔离度,兼顾了计算精度和速度,得到的计算结果和测量值吻合良好。     

机载天线隔离度仿真与分析

天线隔离度是机载电子系统实现电磁兼容预测的重要参数.根据反应积分原理,提出采用孤立天线远场方向图来替代实际环境中天线的办法,简化分析模型,在保证分析精度的基础上,提高计算效率,从而高效完成电大尺寸环境中天线隔离度的仿真分析.通过在自由空间情况下进行测试,验证了仿真结果的准确性.     

基于多物理场耦合分析的机载天线仿真技术研究

为了预测、评估对物理场影响较为敏感的微带阵列天线在复杂的温度、应力等多物理场效应影响下的性能变化规律,本文从多场耦合的机理出发,立足于机载阵列天线装机状态下的复杂物理环境,深入分析阵列天线多物理场效应.通过将求解电大尺寸具有优势的多层快速多极子算法和计算温度场及结构场问题的有限元数值算法的联合运用,成功突破模型关联技术、网格共享技术、多物理场协同仿真控制等关键技术,形成了一套较为成熟的解决天线多物理场仿真中典型电热结构耦合问题的仿真流程.     

机载天线电磁兼容技术分析

随着机载天线的广泛应用,电磁兼容问题的分析非常重要。针对机载天线的特点,首先对机载天线电磁兼容的核心问题和主要解决途径进行了简要介绍,论述了机载天线隔离度的定义及计算方法,对常用的有限元法、物理光学、几何光学和几何绕射理论等天线电磁兼容技术分析方法进行了比较,最后结合飞机系统的具体情况提出采用混合方法分析机载天线电磁兼容技术问题。     

舰载多天线系统电磁兼容性分析

针对舰载多天线系统电磁兼容性(EMC)分析计算量大、耗时过长问题,提出了自动分层的多层快速多极子方法(MLFMA).采用BiCGStab(l)结合近场预条件方法进行求解,进一步提高了MLFMA的综合效率.为了分析天线间的近场耦合特性,运用微波网络理论结合互易定理给出了任意天线间的隔离度表达式.计算并分析了尺度与真实尺寸相当的某舰船模型上多根超短波天线的辐射方向图和天线间的隔离度.数值结果表明算法准确有效,并且对于电大运载平台中天线布局优化具有一定的指导意义.     

MoM-NPO混合算法分析天线-载体系统

安装在电大尺寸平台上的天线,载体的影响不容忽视。采用同样基于电流展开的矩量物理光学混合算法,并通过NURBS物理光学法进一步降低复杂结构的建模和计算,将载体对天线的影响用物理光学电流合并到包括天线的在内较小的矩量法区,使矩量法解决天线-载体系统成为可能。通过矩量法和混合算法分析载体对天线性能的影响证明,载体对天线的影响是显著的,混合算法能够在保证精度的前提下提高运算速度,为解决电大尺寸载体上天线的特性提供了良好的途径。     

区域分裂法在电大尺寸柱体电磁散射中的应用

基于区域分裂算法（ＤＤＭ）提出了一种精确高效的算法。通过沿二维物体表面将原问题分解为若干个相对独立的子问题,使得原问题中的稀疏矩阵变换为各子域中带宽极窄的带状阵,计算时间从Ｏ（Ｎ＾２）下降为Ｏ（Ｎ）。同时,由于每个子域可以单独求解,使内存开销从Ｏ（Ｎ＾２）下降为Ｏ（Ｎ／ｍ）（ｍ为子域个数）,从而可以很好地处理电大尺寸或超大尺寸柱体的散射问题。文中成功地计算了周长为１０万个波长的几个电大尺寸二维柱体     

一种改进的机载卫通天线跟踪算法

在圆锥扫描实现过程分析的基础上,提出了一种改进的机载卫通天线跟踪算法.结合天线方位、俯仰方向图及其三维重构法,对该跟踪算法进行了理论推导.运用Matlab曲线拟合仿真,给出了天线跟踪调整方向和角度的计算公式,最后分析了实际应用中需要注意的一些问题.飞行试验表明,该改进的圆锥扫描跟踪技术提高了天线伺服系统的跟踪精度和跟踪速度,很好地满足了机载卫星通信的需求.     

机载天线辐射特性的并行FDTD分析

利用基于MPI的并行FDTD全波数值分析方法对机载天线辐射问题进行准确快速地分析.通过消息传递MPI平台和区域分割技术进行并行计算,实现了计算资源的扩展,可以对飞机和天线进行精确建模分析.根据网络法提出了FDTD应用于天线间隔离度的分析计算方法,避免了天线隔离度扫频计算的复杂性.数值结果表明该方法的正确性和有效性.以伊尔76飞机为例分析了机载超短波天线的方向图和隔离度.所用方法和分析结果可以作为飞机电磁兼容性设计的手段和依据.     

电大尺寸导体附近线天线的辐射方向图研究

采用一致性几何绕射理论分析了电大尺寸导体对其附近的对称振子天线辐射方向图的影响,计算了在三种不同环境下的天线方向图,结果与软件(FEKO)仿真结果吻合良好.为分析工程中复杂的电大尺寸目标,提供了重要的参考依据.     

A Practical Parallel Algorithm for All-Pair Shortest Path Based on Pipelining

On the basis of Floyd algorithm with the extended path matrix, a parallel algorithm which resolves all-pair shortest path （APSP） problem on cluster environment is analyzed and designed. Meanwhile, the parallel APSP pipelining algorithm makes full use of overlapping technique between computation and communication. Compared with broadcast operation, the parallel algorithm reduces communication cost. This algorithm has been implemented on MPI on PC-cluster. The theoretical analysis and experimental results show that the parallel algorithm is an efficient and scalable algorithm.     

基于MapReduce的并行蚁群算法研究与实现

蚁群算法在处理大规模TSP问题耗时较长,为解决这一不足,给出了一种基于MapReduce编程模式的并行蚁群算法。采用MapReduce的并行优化技术对蚁群算法中最耗时的循环迭代和循环赋值部分进行改进,同时运用PC集群环境的优势将具有一定规模的小蚁群分配到对应的PC机上,使其并行执行,减少运行时间。实验证明改进后的并行蚁群算法在大数据集上运行时间明显缩短,执行效率显著提高。     

空间谱估计测向系统设计

空间谱估计测向是一种以多元天线阵结合现代数字信号处理为基础的新型测向技术。针对空间谱估计中的典型算法——MUSIC算法,在研究超分辨测向系统的构成、工作原理和硬件实现方案的基础上．提出了一种实现MUSIC算法的并行处理方案。     

Pairwise Optimal Weight Realization—Acceleration Technique for Set-Theoretic Adaptive Parallel Subgradient Projection Algorithm

The adaptive parallel subgradient projection (PSP) algorithm was proposed in 2002 as a set-theoretic adaptive filtering algorithm providing fast and stable convergence, robustness against noise, and low computational complexity by using weighted parallel projections onto multiple time-varying closed half-spaces. In this paper, we present a novel weighting technique named pairwise optimal weight realization (POWER) for further acceleration of the adaptive PSP algorithm. A simple closed-form formula is derived to compute the projection onto the intersection of two closed half-spaces defined by a triplet of vectors. Using the formula inductively, the proposed weighting technique realizes a good direction of update. The resulting weights turn out to be pairwise optimal in a certain sense. The proposed algorithm has the inherently parallel structure composed of q primitive functions, hence its total computational complexity O(qrN) is reduced to O(rN) with q concurrent processors (r: a constant positive integer). Numerical examples demonstrate that the proposed technique for r=1 yields significantly faster convergence than not only adaptive PSP with uniform weights, affine projection algorithm, and fast Newton transversal filters but also the regularized recursive least squares algorithm     

一种有效的基于并行量子进化算法的图像边缘检测方法

本文基于费用函数最小化方法,提出一种混合并行量子进化算法用于文本图像的边缘检测。量子进化算法是一种基于量予计算的概念和理论(诸如量子比特和量子叠加态)的进化算法,它采用了量子编码来表征染色体,由于量子比特的概率表示,能够表示出解的线性叠加状态。此外,量子进化算法具有收敛快和好的全局搜索特性,因此它比传统的进化算法更适于并行结构的实现。我们将这一算法和局部搜索算法相结合,用于图像的边缘检测问题,得到了令人满意的检测效果,并对噪声有较好的抑制作用。     

Image recovery using partitioned-separable paraboloidal surrogatecoordinate ascent algorithms

Iterative coordinate ascent algorithms have been shown to be useful for image recovery, but are poorly suited to parallel computing due to their sequential nature. This paper presents a new fast converging parallelizable algorithm for image recovery that can be applied to a very broad class of objective functions. This method is based on paraboloidal surrogate functions and a concavity technique. The paraboloidal surrogates simplify the optimization problem. The idea of the concavity technique is to partition pixels into subsets that can be updated in parallel to reduce the computation time. For fast convergence, pixels within each subset are updated sequentially using a coordinate ascent algorithm. The proposed algorithm is guaranteed to monotonically increase the objective function and intrinsically accommodates nonnegativity constraints. A global convergence proof is summarized. Simulation results show that the proposed algorithm requires less elapsed time for convergence than iterative coordinate ascent algorithms. With four parallel processors, the proposed algorithm yields a speedup factor of 3.77 relative to single processor coordinate ascent algorithms for a three-dimensional (3-D) confocal image restoration problem.     

RLS-based adaptive algorithms for generalized eigen-decomposition

The aim of this paper is to develop efficient online adaptive algorithms for the generalized eigen-decomposition problem which arises in a variety of modern signal processing applications. First, we reinterpret the generalized eigen-decomposition problem as an unconstrained minimization problem by constructing a novel cost function. Second, by applying projection approximation method and recursive least-square (RLS) technique to the cost function, a parallel adaptive algorithm for a basis for the r-dimensional (r>0) dominant generalized eigen-subspace and a sequential algorithm based on deflation technique for the first r-dominant generalized eigenvectors are derived. These algorithms can be viewed as counterparts of the extended projection approximation subspace tracking (PAST) and PASTd algorithms, respectively. Furthermore, we modify the parallel algorithm to explicitly estimate the first r-generalized eigenvectors in parallel, not the generalized eigen-subspace. More important, the modified parallel algorithm can be used to extract multiple generalized eigenvectors of two nonstationary sequences, while the proposed sequential algorithm lacks this ability because of slow convergence of minor generalized eigenvectors due to error propagation of the deflation technique. Third, following convergence analysis methods for PAST and PASTd, we prove the asymptotic convergence properties of the proposed algorithms. Finally, computer simulations are performed to investigate the accuracy and the speed advantages of the proposed algorithms.     

Fast image recovery using dynamic load balancing in parallelarchitectures, by means of incomplete projections

This paper formulates an incomplete projection algorithm that is applied to the image recovery problem. The algorithm allows an easy implementation of dynamic load balancing for parallel architectures. Furthermore, the local computation-communication load ratio can be adjusted, since each processor performs a finite number of iterations of any projection-type technique, and this number can be provided as a parameter of the algorithm. Numerical results compare favorably with those obtained by the extrapolated method of parallel subgradient projections.     

机群系统上基于Hashing的多目标串匹配并行算法

基于孙子定理构造均匀的Hash函数并继承Karp-Rabin模式匹配思想，利用“筛选”方法，给出一种机群系统上的多目标串匹配并行算法。通过预处理将字符串映射成惟一的一对整数值，采用比较一对整数值来取代逐个字符比较字符串的方法使得匹配过程快速且比较结果是确定的：“筛选”节省了比较时间。算法分析和实验结果表明该并行算法简明、高效和可扩展。     

并行算法在VLSICAD中的应用

本文简介并算法在ＶＬＳＩＣＡＤ中的应用，介绍了这一领域的研究现状，所采用的关键技术以及进一步的研究发展方向。