DSPAndMatlab

1.本程序是自己刚刚接触SAR时编写的时域星载SAR点目标回波仿真，包含了卫星轨道模型、计算星目斜距时用到的各种坐标系转换、SAR回波计算模型以及简单的SAR成像模型，希望对初学者有所帮助。 2.相应的参考资料可查找《卫星轨道姿态动力学与控制》及《魏钟铨_合成孔径雷达卫星》；(1. This program is just contact with their own prepared when the time domain SAR spaceborne SAR point target echo simulation, contains the satellite orbit model, the satellite project when the use of a variety of slant range coordinate system conversion, SAR echo calculation model and SAR imaging model simple, want to help beginners. 2. The corresponding reference can be to find " satellite attitude and orbit dynamics and control" and " Wei Zhong Quan _ synthetic aperture radar satellites" )

关于雷达信号处理的教程，详细介绍了雷达信号的分类，以及处理方式(On radar signal processing tutorial, detailing the classification of radar signals, as well as treatment modalities)

对涡旋光场光波传输进行仿真，对于不同拓普电荷情况仿真输出。(Lightwave transmission vortex light field simulation for different situations TOP charge simulation output.)

基于累计量(SHIBBS)的实信号盲分离算法(Based on the cumulative amount of (SHIBBS) the real signal separation algorithm)

一、 本程序采用16QAM调制方式，对一串2进制信源进行调制，用升余弦滚降函数进行基带调制，再调到高频信道；在信道上加入高斯白噪声，运用匹配滤波器解调，画出解调星座图，运用最小欧氏距离译码判决，计算误比特率。(First, the program uses 16QAM modulation, the source of a string of two binary modulation, with the Raised Cosine modulated baseband functions, and then transferred to high-frequency channel in the channel by adding Gaussian white noise, the use of matched filter demodulation, demodulation constellation plot using the minimum Euclidean distance decoding decision, bit error rate calculation.)

基于傅里叶变换的混沌图像置乱加密方法及其代码实现(Fourier transform based on chaotic image encryption and scrambling code implementation)

说明：  仿真了AM信号的产生过程和方法，过程详尽，方法简单，已经过测试。(Simulation of the AM signal generation process and methods, processes and detailed, the method is simple, has been tested.)

捷联惯导方程矩阵在matlab环境下的仿真源代码程序(SINS matrix equation matlab source code under the simulation program)

一个利用matlab Robust toolbox设计机械臂的实例，有详细的解释和源代码，是学习机器人鲁棒控制的很好的例子。(A design using matlab Robust toolbox manipulator example, a detailed explanation and source code, is to study robust control of robot good example.)

The method works as follows. 1. For a data set with dimensionality, d, compute the variance of data in each dimension (column). 2. Find the column with maximum variance call it cvmax and sort it in any order. 3. Divide the data points of cvmax into K subsets, where K is the desired number of clusters. 4. Find the median of each subset. 5. Use the corresponding data points (vectors) for each median to initialize the cluster centers.