matlab_GUI

matlab的信号处理工具箱；内含高阶谱函数，如双谱等，使用很方便(matlab toolbox,hosa,Higher-Order Spectral Analysis Toolbox)

volterra series 核函数 matlab 程序(volterra series kernel)

说明：  120多个用MATLAB编写的信号处理程序，它们是本书各个章节的大部分例题。用MATLAB编写的程序的名称由“exa”开头，接下来是所在的章、节及例题的序号，如exa010101,指的是第1章第1节（即1.1节）的第1个例题，即例1.1.1。如果该程序是为了说明某一个m文件的应用，则在上述名称的后面跟一个下划线，再在后面加上所说明的MATLAB文件的名称，如exa011001_rand，即是例1.10.1,该例用来说明rand.m文件的应用。(More than 120 signal processing programs written in MATLAB are most of the examples in each chapter of this book. The name of the program written in MATLAB begins with "exa", followed by the ordinal number of the chapters, sections and examples, such as exa0101, which refers to the first example of chapter 1, section 1 (i.e. section 1.1), namely example 1.1.1. If the program is to illustrate the application of an M file, then follow the above name with an underscore, and then add the name of the MATLAB file, such as exa011001_rand, which is Example 1.10.1. This example is used to illustrate the application of rand.m file.)

说明：  PV generation model which was build by matlab/simulink

the matlab book answersthe matlab book answers(the matlab book answers!the matlab book answers!the matlab book answers!)

比较传统单用户检测、线性解相关多用户检测、最小均方误差多用户检测的误码率函数MUD.m(mud.m)

主要是MIMO-OFDM系统的C++语言实现，简单实用，值得一看。(This is a C++ language of MIMO-OFDM system, simple and practical, worth a visit.)

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详细阐述如何发布matlab程序的方法，尤其适合GUI界面的发布，叙述详细到位。(Elaborate method of how to publish matlab program, especially for the release of a GUI interface, described in detail in place.)

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)