Compressive-Sensing

这是用matlab编写的常用概率算法,非常有用,欢迎下载哈!(This is commonly prepared using matlab probability algorithm, very useful, welcome to download and Kazakhstan!)

window下 出口同步通信工具，提供c＋＋源码，说明详细(window exporting synchronous communication tools, c++ source code, specify details)

窄带相关脉冲估计多普勒频移(包括解模糊算法)(Narrowband related pulse Doppler shift estimation (including solution fuzzy algorithm))

编写M文件，创建如下图形窗口，分别绘制如下图所示的曲线。 （1）曲线y=sin(t)sin(9t)，要求采用红色，点线。横坐标取值范围[0~ ]，标注x轴、y轴并加标题。 （2）一组余弦曲线，其振幅分别为0.4，0.6和0.8。横坐标取值范围[0~2 ]。添加图例与标题。进入图形编辑状态，调整图例的位置。 （3）分别绘制peaks函数的曲面图和网格线图。对曲面图做平滑处理，并添加标题与colorbar。 (Write M file, create the following graphics window, curves were plotted as shown below. (A) the curve y = sin (t) sin (9t), require the use of a red dotted line. The abscissa in the range [0 ~  ], labeled x-axis, y-axis and a title. (2) a set of a cosine curve, and the amplitude was 0.4, 0.6, 0.8. Abscissa value range [0 ~ 2  ]. Add a legend and title. Into the graphical editing mode, adjust the position of the legend. (3) were drawn surface chart peaks function and grid line graph. Surface charts do for smoothing and add titles and colorbar.)

说明：  自建了一个matlab-simulink单机无穷大系统模型，用于进行简单的单机无穷大系统仿真实验(Self-built a matlab-simulink single-machine infinity system model for simple single-machine infinite system simulation experiments)

TextureAnlysis.m实现遥感图像的纹理分析，以 方向邻域内的灰度均值 和 灰度共生矩阵的熵 作为纹理特征，使用k-means聚类。(TextureAnlysis.m the realization of remote sensing image texture analysis to the direction of the gray-scale neighborhood of the mean and the entropy of gray level co-occurrence matrix as texture features, the use of k-means clustering.)

用于画海洋中常用的流矢过程线、潮流玫瑰图等，非常实用(Commonly used for painting the ocean flow vector process line, the trend of roses and so on, very useful )

说明：  MATLAB 编写的用matlab 来实现的pde图像增强方法，如果不对的地方，请各位指出。(This is an enhance image example to use pde ways,if there is some problems ,please tell me )

mathlab code for tdoa demo

采用经典的数据关联方法算法实现的对单目标和多目标的跟踪(Classic data association algorithm was adopted to realize the track of single objective and multiple objectives)