相控阵雷达资源受限时最优搜索性能研究

多功能相控阵雷达通过对时间和能量资源的合理调度,可同时完成目标搜索、跟踪等多项任务。但对优先级别较低的搜索任务来说,当高优先级任务抢占雷达资源时,将会导致搜索性能下降。分析了影响相控阵雷达搜索性能的主要因素,详细研究了在搜索资源受限条件下,相控阵雷达搜索性能的优化模型,给出了搜索帧周期和探测距离计算的最佳准则和方法。通过仿真实验,分析了优化后相控阵雷达的搜索性能,实验结果验证了该模型的有效性。     

Fundamentals of the analytic network process — Dependence and feedback in decision-making with a single network

The Analytic Network Process (ANP) is a multicriteria theory of measurement used to derive relative priority scales of absolute numbers from individual judgments (or from actual measurements normalized to a relative form) that also belong to a fundamental scale of absolute numbers. These judgments represent the relative influence, of one of two elements over the other in a pairwise comparison process on a third element in the system, with respect to an underlying control criterion. Through its supermatrix, whose entries are themselves matrices of column priorities, the ANP synthesizes the outcome of dependence and feedback within and between clusters of elements. The Analytic Hierarchy Process (AHP) with its independence assumptions on upper levels from lower levels and the independence of the elements in a level is a special case of the ANP. The ANP is an essential tool for articulating our understanding of a decision problem. One had to overcome the limitation of linear hierarchic structures and their mathematical consequences. This part on the ANP summarizes and illustrates the basic concepts of the ANP and shows how informed intuitive judgments can lead to real life answers that are matched by actual measurements in the real world (for example, relative dollar values) as illustrated in market share examples that rely on judgments and not on numerical data. Thomas L. Saaty holds the Chair of University Professor, Katz Graduate School of Business, University of Pittsburgh, Pittsburgh, PA, and obtained his Ph.D. in mathematics from Yale University. Before that he was a professor at the Wharton School of the University of Pennsylvania for ten years. Prior to that he spent seven years at the Arms Control and Disarmament Agency in the State Department in Washington, DC, that carried out the arms reduction negotiations with the Soviets in Geneva. His current research interests include decision-making, planning, conflict resolution and synthesis in the brain. As a result of his search for an effective means to deal with weapons tradeoffs at the Disarmament Agency and, more generally, with decision-making and resource allocation, Professor Saaty developed The Analytic Hierarchy Process (AHP) and its generalization to dependence and feedback, the Analytic Network Process (ANP). He is co-developer of the software Expert Choice and of the software Super Decisions for decisions with dependence and feedback. He has authored or co-authored twelve books on the AHP/ANP. Professor Saaty has also written a number of other books that embrace a variety of topics, including Modern Nonlinear Equations, Nonlinear Mathematics, Graph Theory, The Four Color Problem, Behavioral Mathematics, Queuing Theory, Optimization in Integers, Embracing the Future and The Brain: Unraveling the Mystery of How It Works. His most recent book is Creative Thinking, Problem Solving & Decision Making. The book is a rich collection of ideas, incorporating research by a growing body of researchers and practitioners, profiles of creative people, projects and products, theory, philosophy, physics and metaphysics...all explained with a liberal dose of humor. He has published more than 300 refereed articles in a wide variety of professional journals. He has been on the editorial boards of Mathematical Reviews, Operations Research, Naval Research Logistics Quarterly, Mathematical and Computer Modeling, Socio-Economic Planning Sciences, Applied Mathematics Letters, and several others. He also served as consultant to many corporations and governments.     

Optimizing on multiple constrained QoS multicast routing algorithms based on GA

With the rapid development of Internet, mobile networks and high-performance networking technology,multiple constrained QoS multicast routing optimization in networks with uncertain parameters has become a very important research issue in the areas of networks and distributed systems. It is also a challenging and hard problem to the next generation Internet and high-performance networks, and has attracted the interests of many people. This paper discusses the multiple constrained QoS multicast routing problem, which may deal with the delay, delay jitter,bandwidth and packet loss metrics, and describes a network model for researching the routing problem. The paper mainly presents multiple constrained QoS multicast routing algorithm (MCQMRA), a QoS multicast routing policy for Internet,mobile network or other high-performance networks, which is based on the genetic algorithm (GA) and can provide QoS-sensitive paths in a scalable and flexible wayin the network environment with uncertain parameters. The MCQMRA can also optimize the network resources such as bandwidth, delay, packet loss metrics and can converge to the optimal or near-optimal solution within few iterations, even for the network environment with uncertain parameters. Simulation results show that MCQMRA is an available approach to QoS multicast routing decision.     

一种新型椭圆曲线密码系统协处理器的VLSI设计

研究了椭圆曲线运算的方法及椭圆曲线密码系统协处理器VLSI实现问题。基于一种新的投影坐标系,给出了求解椭圆曲线点乘运算的点加与倍点算法,提出了一种新的优化存储单元调度方案。根据研究的算法,得到了一种新型的协处理器VLSI结构。仿真结果表明,在50MHz时钟下,点乘运算速度为平均155次/秒。     

降水预测的模糊权马尔可夫模型及应用

中长期降水量的预测是气象科学的一个难点问题，论文首先基于降水过程存在大量不确定性、不精确性的特点，应用模糊有序聚类的方法建立降水丰桔状况的分级标准；然后针对降水量为相依随机变量的特点，采取以规范化的各阶自相关系数为权重，用加权的马尔可夫链来预测未来降水的丰桔变化状况；在此基础上，根据模糊集理论中的级别特征值，计算出具体的降水量；最后以山西省河曲水文站近50年的降水资料为实例，对该方法进行了具体的应用，收到了较为满意的结果，从而为提高中长期降水量预报的精度提供了一条值得探索的途径。     

基于四叉树的地形数据二维显示与交互

地形是合成自然环境中重要的组成部分，地形的二维显示对于创建想定以及二雏仿真具有重要的意义。本文根据地形二维显示中遇到的实际问题，提出了基于四叉树的地形二维显示框架，将不同类型、各种规模、各种格式、大数据量的地形数据用该显示框架统一组织起来，实现地形图的动态生成，解决了地形图的动态交互显示及实时查询问题。     

基于Hopfield网络单元机组负荷多变量模型算法控制

电厂负荷系统是一个有耦合和约束的多变量对象。模型算法控制(MAC)是预测控制的一种，在惯性迟延对象的控制中有很好的应用。该文提出用Hopfield网络来求解具有约束的多变量负荷系统的控制输入。仿真表明，该方法算法简单、鲁棒性强、控制效果理想。     

用于组合测姿的Kalman渐消因子自适应估计算法

提出了适用于姿态测量的Kalman滤波渐消因子自适应估计算法,滤波中采用序贯处理的方法计算出每个量测量对应的渐消因子,在位置速度组合导航系统中,只有位置、速度的误差状态是直接可观测的,用序贯滤波处理计算得到的渐消因子对协方差阵中对应于位置和速度误差状态的对角元素进行自适应控制,抑制滤波发散,提高位置、速度和姿态的测量精度。半实物仿真表明,与原来的算法相比,修改后的方法不仅能够提供高精度位置、速度信息,而且还可以提供高精度姿态信息,其中航向误差在0.08°以下,俯仰和横滚误差在0.02°左右。     

助推钻地弹对混凝土目标侵彻效应的数值模拟

为设计助推钻地弹选择合适的助推方案,采用LS-DYNA程序进行了三维数值模拟研究。通过将动能弹侵彻混凝土数值模拟的结果与试验数据进行对比,确定了合理的有限元仿真模型,选取了合适的材料模型和参数。结合某结构助推钻地弹的研制工作,对助推钻地弹侵彻混凝土的效应进行了数值模拟,与试验数据进行比较,得出数值模拟能够较好地反映助推钻地弹对混凝土目标的侵彻效应。通过对各种助推方案的数值模拟,得出了选择助推方案的一般原则,为新型钻地弹的研制提供了指导。     

GPS抗干扰单星约束自适应滤波器组设计与仿真

提出了一种分别针对多颗GPS卫星的波束形成自适应抗干扰实现方法,称为单星约束自适应滤波器组方法,用于GPS抗干扰处理,各个滤波器使阵列天线形成的主瓣分别对准每个期望卫星,从而提高接收信干噪比。该方法的自适应部分通过Givens旋转实现,并利用Givens旋转的结构特点,共用了大部分运算,降低了单星约束滤波器组的运算量。相比多星约束方法,能够提高信干噪比,并且计算量不会显著增加。仿真结果表明该方法相比多星约束方法,能显著提高信干噪比。