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说明：  数字信号处理实验（matlab版）书的参考程序 MATLAB例题文件夹(Digital Signal Processing Experiment (matlab version) reference book example MATLAB program folder)

锁相环和锁频环在数字costas环中的应用.pdf 一篇关于costa环路的新颖设计方案，包含大量的仿真图和性能分析，对学习锁相环有很大帮助(And frequency-locked loop PLL digital costas loop in the application. Pdf a novel about the costa loop design, contains a large number of simulation map and performance analysis are very helpful for learning PLL)

生成正弦波，不需要调用sin函数，利用的查表法(Generate a sine wave, you do not need to call the sin function using table look-up method)

毕业设计有用，供做算法研究人员参考，小波包分析提取振动信号中的特征频率。( Graduation useful Algorithm for researchers to do reference, Wavelet packet analysis to extract vibration signal characteristic frequency.)

说明：  MATLAB有限元分析与应用的源代码，非常实用！(MATLAB finite element analysis and application source code, very useful!)

采用加窗法在matlab中进行功率谱密度计算(Windows and method for power spectral density calculation in matlab)

应用matlab编程实现基于卡尔曼滤波的目标运动 小球的实时轨迹跟踪(using matlab : using kalman to track the target running ball in time)

此文档为matlab7.0简明实例教程，对初学matlab的人很有帮助，上米娜有很多简单例子(This document matlab7.0 concise tutorials from beginner matlab helpful on Mina, there are many simple examples)

基于直方图的分布估计算法matlab源程序，测试函数选用的是30维函数(for h=1:m for j=1:((x2-x1)/binswidth) if (x(h)>=x1+(j-1)*binswidth)&(x(h)<x1+j*binswidth) H(j)=H(j)+1 end end end [wid,len]=size(H) H=H/(wid*len) objvalue=sort(objvalue, descend ) for j=1:((x2-x1)/binswidth))

MUSIC算法是一种基于矩阵特征空间分解的方法。从几何角度讲，信号处理的观测空间可以分解为信号子空间和噪声子空间，显然这两个空间是正交的。信号子空间由阵列接收到的数据协方差矩阵中与信号对应的特征向量组成，噪声子空间则由协方差矩阵中所有最小特征值（噪声方差）对应的特征向量组成。MUSIC算法就是利用这两个互补空间之间的正交特性来估计空间信号的方位。噪声子空间的所有向量被用来构造谱，所有空间方位谱中的峰值位置对应信号的来波方位。MUSIC算法大大提高了测向分辨率，同时适应于任意形状的天线阵列，但是原型MUSIC算法要求来波信号是不相干的。(MUSIC algorithm is a matrix decomposition method based on feature space. From the geometric point of view, the observed spatial signal processing can be decomposed into signal subspace and noise subspace, it is clear that the two spaces are orthogonal. The signal received by the array to the subspace of the covariance matrix of the signal component corresponding eigenvectors, the noise subspace of the covariance matrix of all by the smallest eigenvalue (noise variance) eigenvectors corresponding to the composition. MUSIC algorithm is the use of orthogonal properties between these two complementary space to estimate the spatial orientation of the signal. Noise subspace of all vectors are used to construct the spectrum, all the spatial orientation of the spectrum corresponding to the peak position of the signal wave direction. MUSIC algorithm greatly improves the resolution measurements, while the antenna array adapted to any shape, but the prototype MUSIC algorithm requires to wave sign)