OFDM_DOPPLER_ML

阵列信号处理中关于DOA估计和信源数估计的几个经典算法的仿真(the simulation of some classic algorithms on array signal processing for DOA and the number of sources)

基于一篇文章的代码实现-结合场景信息进行的目标分类（文章及文件名）(An article based code implementation- combined with scenes of target classification information (articles and file name))

kmeans is image segmentation algorithm.

物体围绕一圆周运动，角加速度是白噪声，观测噪声是高斯白噪声。使用卡尔曼滤波器实现物体的跟踪(Follow the trace)

用matlab语言实现信号的盲源分离，功能比较小(Signal blind source separation)

pdfs regrding matlab

说明：  平均周期图法和平滑平均周期图法估计功率谱 从经典功率谱估计周期图法原理入手，从理论上分析了其存在的局限性，借助Welch算法对其进行修正。依靠Matlab强大的数值分析和信号处理能力进行实验!! 用周期图法和welch法分别估计了一下信号的功率谱密度。采样频率200Hz，时间序列长度2048，FFT数据点数2048。其中welch法使用hanning窗，窗口长度600．(Average periodogram and periodogram smoothing method to estimate the average power spectrum from the classical power spectrum estimation periodogram principle starting from the theoretical analysis of the limitations of its existence, with the Welch algorithm, amending them. Rely on the powerful Matlab numerical analysis and signal processing capabilities to experiment!! With periodogram and welch method were estimated signal power spectral density about. Sampling frequency of 200Hz, the time sequence length 2048, FFT data points 2048. Welch method which uses hanning window, the window length of 600.)

此程序为在MATLAB环境下编写的模糊控制模块，通过模糊判断改变ＰＩＤ控制的参数，实现模糊ＰＩＤ控制。(This program is written in the MATLAB environment, fuzzy control module to change the PID control parameters, the fuzzy PID control, fuzzy judgment.)

本书详细讲述了如何用MATLAB进行程序设计，如何编写清楚、高效的程序。书中强调了自上而下的程序设计方法、函数的使用、MATLAB内部工具的使用和数据结构，并指出了一些使用技巧和编程者常犯的错误。(This book describes in detail how to use MATLAB for programming, how to write clear and efficient procedures. The book emphasizes top-down program design, function, use, MATLAB uses internal tools and data structures, and pointed out some tips and common mistakes programmers.)

采用卡尔曼滤波器来对视频中的目标进行跟踪，带有一个产生视频的主函数，通过kalman滤波器实现对目标的跟踪，简单易懂(Kalman filter to track targets in the video, produced with a video of the main function, to achieve the target tracking by kalman filter, easy to understand)