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说明：  关于gauusian函数的matlab代码，角点提取时会用到(About gauusian function matlab code that will be used corner detection)

MIMO雷达模型下一种子空间谱估计方法，采用过估计的方法，以避免信源数估计的问题，直接对数据协方差矩阵进行变换，从而构造了信号子空间投影矩阵和噪声子空间投影矩阵，不需要像经典的MUSIC一样对其进行特征分解，完全避开了在一般非理想情况下MUSIC算法必须面对的识别小特征值与大特征值的麻烦，降低了复杂度，而且该方法不受快拍数的影响，在相干源情况下也能准确的估计目标的入射角，不会出现伪峰。(A subspace based DOA (Direction-Of-Arrival) estimation method for MIMO (Multiple-Input Multiple-Output) radar is studied in this paper. By transforming the data covariance matrix, we can construct the projection matrix of signal subspace and noise subspace. The computation complexity of the algorithm is reduced as it does not need to perform eigendecomposition. By using the over estimated theory, this algorithm avoid estimating the number of signal sources and can keep a good estimate performance in the case of lower SNR (Signal-to-Noise Ratios) and multi-target. )

目的：实现多gui间的通信。 1. 由gui1打开gui2，并从gui2读取数据画图； 2. 数据包的传递：在gui1定义一个数据包，包括字符串，数据，把数据包传递给gui2并由其更新，gui2更新好的数据在传回gui1并显示出来。（注：程序中加黑划线部分为实现预期功能的要点） (Purpose: communication gui. Method: 1. Opened by gui1 gui2, and to read data from gui2 drawing (2) the transfer of the packet: a packet defined in gui1, including strings, data packets passed to gui2 updated by gui2 the updated data in the returned gui1 and displayed. (Note: the program plus the the black underlined part to achieve the desired functionality of the points))

high gain observer article

说明：  比较全面的MATLAB编程教案，共十四个PPT涵盖MATLAB编程各方面。(MATLAB programming more comprehensive lesson plans, a total of fourteen PPT covers all aspects of MATLAB programming.)

说明：  《精通Matlab与C/C++混合程序设计》（ 第二章） 刘维编著("proficient Matlab and C/C mixed Program Design" (Chapter II) Liu compilation)

说明：  caculate RS exponent

Sine pdf for users that work in the domain of signal processing. help how to improve the process and work

基于PCA算法的matlab源代码，适合初学者，可以快速学习PCA方法(Based on PCA algorithm matlab source code, suitable for beginners, you can quickly learn PCA methods)

Backpropagation, an abbreviation for backward propagation of errors , is a common method of training artificial neural networks used in conjunction with an optimization method such as gradient descent. The method calculates the gradient of a loss function with respects to all the weights in the network.