STBCQPSK-matlab

带时延的纯方位角单站跟踪算法,它有效地解决了用EKF算法进行纯方位角跟踪时可能出现的不稳定和滤波发散现象以及声音信号的时延问题。(Pure time delay with a single station azimuth tracking algorithm, which effectively solved the EKF algorithm with pure azimuth tracking of the instability that may arise and filtering divergence phenomenon, as well as the voice signal delay problem.)

image source method old

一种基础的恒模算法，应用于信道补偿的盲均衡(a basic algorithm for costant modulus algorithm,the blind equlization application used to channel conpasention)

利用matlab分离多声道音频，并显示分离的多个单声道波形。(The separation of multichannel audio using matlab, and display multiple mono waveforms of separation )

产生供选择的16qam 64qamOFDM信号(Generating a signal for the selected 16qam 64qamOFDM)

先归一化输入输出参数，用KMens优化RBF神经网络中心值，然后计算隐节点数据中心间的距离（矩阵），得到各隐节点的扩展常数（宽度），接着用最小二乘法得到各隐节点的输出权值，对数据进行反归一化，并绘图得到神经网络输出与测试集图，同时进行性能评价(First, the input and output parameters are normalized, the central value of the RBF neural network is optimized by KMens, then the distance (matrix) between the data centers of the hidden nodes is calculated, and the extended constant (width) of the hidden nodes is obtained. Then the output weights of the hidden nodes are obtained by the least square method, and the data are back normalized, and the neural network is drawn. Output and test set diagrams, and performance evaluation)

这里实现了图像除噪声的算法，各种模板算子。(Here to realize the image noise than the algorithm, various template operator.)

采用动态加权综合评价方法建立评价模型，增大了评价结果的客观性。首先对评价指标数据进行归一化处理，然后选取偏大型正态分布函数作为动态加权函数建立评价模型，从而对评价指标观测值进行排序，最后用决策(Dynamic weighted comprehensive uation method to establish uation model, increasing the objectivity of uation results. First uation data normalization, and then the partial large normal distribution function as a dynamic weighting function uation model, and thus the uation observations are sorted, with the final decisions)

MATLAB系统仿真实例教程MATLAB simulation examples tutorial(MATLAB simulation examples of MATLAB simulation examples tutorial tutorial)

主要模拟的是信号光在增益光纤中放大的过程(Main simulation signal light is amplified in the gain fiber in the process)