bjp2008_1_68-77

稀疏表示doa估计，l1范数凸优化的算法，性能很好(Low complexity method for DOA estimation using array covariance matrix sparse representation)

matlab 和java的Runner程序(The Runner matlab and java program)

介绍了《MATLAB语言常用算法程序集》一书的源程序(" MATLAB language commonly used algorithm for assembly," the source book)

旅行者问题（travelling salesman problem）算法()

说明：  2D的FDTD程序，有关边界的设定，物理参数的设定等，对学习FDTD很有帮助。(2D of the FDTD program, setting the boundaries, the physical parameters of the set and so helpful in learning FDTD.)

实现数据的kmeans聚类。对SD，AD及其他多位数据集具有良好的聚类效果。(realize kmeans on data sets and have high qunlity.)

可用于声纳、雷达信号处理的波束形成程序，利用相移法对多信源目标进行方位估计。(Can be used for sonar, radar signal processing beamforming procedure, and use the phase shift method for multi-source target position is estimated.)

在一定的约束条件下，其输出与一给定函数（通常称为期望输出）的差的平方达到最小，通过数学运算最终可变为一个托布利兹方程的求解问题。维纳滤波器又被称为最小二乘滤波器或最小平方滤波器，目前是基本的滤波方法之一。维纳滤波是利用平稳随机过程的相关特性和频谱特性对混有噪声的信号进行滤波的方法，1942年美国科学家N.维纳为解决对空射击的控制问题所建立，是40年代在线性滤波理论方面所取得的最重要的成果。(Under some constraint conditions, the output with a given function (often referred to as expected output) the square of the difference between the minimum, through mathematical operation can be turned into a final taub Leeds equation to solve the problem.Wiener filter is also known as least squares filter or least square filter, is now one of the basic filter method.Wiener filtering is to use the related characteristics of stationary random process and spectrum characteristics of mixed with noise signal filtering method, scientists in the United States in 1942 n. wiener established to solve the control problem of empty shooting, is 40 s in the theory of linear filtering is the most important results.)

= lorentzfit （× ） lorentzfit 符合输入（× ）与罗伦兹功能的形式 “ （× ）= 1 / （（- 2）^ 2 + 3号） 输入 ：因变量值[ 是] （× ） × ：独立变量的值 输出功率 一：名单[ 1 2 3 ] 罗伦兹参数 示例 × = - 16:0.1:35 = 19.4 / （（- 7）^ 2 + 15.8）+ randn （大小（× ）/ 10 ） = lorentzfit （× ） 杰瑞德·威尔斯 11 / 15 / 11 杰瑞德威尔斯[ 点] [在] [ 点] 教育公爵(a = lorentzfit(y,x) LORENTZFIT fits the input Y(X) with a Lorentzian function of the form Y (X) = A1./((X- A2).^2+ A3) INPUTS Y : Dependent variable values [Y(X)] X : Independent variable values OUTPUT A : List of Lorentzian parameters [A1 A2 A3] EXAMPLE x =-16:0.1:35 y = 19.4./((x- 7).^2+ 15.8)+ randn(size(x))./10 a = lorentzfit(y,x) Jered R Wells 11/15/11 jered [dot] wells [at] duke [dot] edu)

ESPRIT算法，MUSIC算法，一阶前向预测算法比较(ESPRIT algorithm, MUSIC algorithm, first-order forward prediction algorithm comparison)