tianzige_word

用MatLab环境进行OFDM仿真代码，具有注释详细，非常适合初学者。(source code for OFDM ,suitable for the beginner)

奥本海姆著作信号与系统 课后答案详细解析 (the references of the book signals and systems)

用Durbin递归求预测误差滤波器和它的输出，求解一维信号反卷积(Use Durbin recursive prediction error filter and its output, solving one-dimensional signal deconvolution)

用于计算两组变量之间的同步指数，用matlab编写的代码(Two sets of variables used to calculate the index synchronization between the code written in matlab)

说明：  洗衣机的模糊控制的matlab实现小Demo(Fuzzy Control of Washing Machine Based on MATLAB Demo)

We address the problem of blind carrier frequency-offset (CFO) estimation in quadrature amplitude modulation, phase-shift keying, and pulse amplitude modulation communications systems.We study the performance of a standard CFO estimate, which consists of first raising the received signal to the Mth power, where M is an integer depending on the type and size of the symbol constellation, and then applying the nonlinear least squares (NLLS) estimation approach. At low signal-to noise ratio (SNR), the NLLS method fails to provide an accurate CFO estimate because of the presence of outliers. In this letter, we derive an approximate closed-form expression for the outlier probability. This enables us to predict the mean-square error (MSE) on CFO estimation for all SNR values. For a given SNR, the new results also give insight into the minimum number of samples required in the CFO estimation procedure, in order to ensure that the MSE on estimation is not significantly affected by the outliers.

This source code is used to import data file

gold序列生成的matlab代码源程序(gold matlab)

基于TOA测量的Newton-Raphson定位算法仿真 基于TOA测量的Gauss-Newton定位算法仿真 基于TOA测量的Steepest Descent定位仿真(TOA-based localization algorithm simulation based on TOA measurements Gauss-Newton measured TOA measurements based on the Newton-Raphson localization algorithm simulation Steepest Descent positioning simulation)

费歇尔准则要求类间均值差异最大而类内的离差平方和最小。(Fischer guidelines require the mean difference between the class and the maximum and minimum sum of squares within the class.)