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这是我自己编写的一段VDkalman程序，对于研究雷达目标跟踪的朋友应该有点帮助，愿意分享。(This is what I have written for some VDkalman procedures, the study of radar target tracking should be a little friend to help, willing to share.)

说明：  用传输矩阵法，模拟取样光纤光栅的反射谱特性(using transfer matrix method to simulate the reflection of sample fiber grating.)

八单元线阵的DOA估计的MUSIC算法实现。可以看出music方法分辨率较高。这里用的是36度方向下的weight,自己也可以尝试其他的天线weight值。 (eight modules Array DOA MUSIC algorithm. Music can be seen higher resolution method. Here is the direction of 36 degrees under the weight, they could also try to the other antenna weight values.)

基于模糊控制的路边平行停车仿真matlab源代码，对于学习模糊控制理论有帮助。(Parallel parking simulation matlab source code is based on fuzzy control roadside, for learning fuzzy control theory help.)

说明：  （1）应用9×9的窗口对上述图象进行随机抽样，共抽样200块子图象； （2）将所有子图象按列相接变成一个81维的行向量； （3）对所有200个行向量进行KL变换，求出其对应的协方差矩阵的特征向量和特征值，按降序排列特征值以及所对应的特征向量； （4）选择前40个最大特征值所对应的特征向量作为主元，将原图象块向这40个特征向量上投影，所获得的投影系数就是这个子块的特征向量。 （5）求出所有子块的特征向量。 ((1) the application of 9 × 9 window of these images at random, a total sample of 200 sub-image (2) all sub-images according to out-phase into a 81-dimensional row vector (3) all 200 lines for KL transform vector, derived its corresponding covariance matrix of eigenvectors and eigenvalues, in descending order by eigenvalue and the corresponding eigenvector (4) a choice to 40 corresponding to the largest eigenvalue eigenvector as the PCA, the original image block to the 40 feature vectors on the projection, the projection coefficients obtained by this sub-block eigenvector. (5) calculated for all sub-block eigenvector.)

MATLAB source code. You can find useful address here.

数学交互界面，可以实现求解线性方程组，三维绘图，二重积分等(Math interactive interface for solving linear equations, three-dimensional drawing, the double integral)

该程序汇编水平集拓扑优化的GUI，学者可以通过人机界面进行结构的拓扑优化(lever-set )

一种最大Lyapunov指数的改进算法，以交通流混沌判别为例(Improved algorithm for maximum Lyapunov exponent, for example in order to determine the chaotic traffic flow)

BPAdaboost，包括主程序，测试集等等(BPAdaboost, including the main program, test sets, etc.)