matlab learning courseware, content, basic, easy to understand, convenient self

this is document to learn a simple way to design a graphic interface in matlab.

在多径信号下的采用toeplitz来进行矩阵重构，对信号到达角进行估计(Using Toeplitz matrix reconstruction, DOA estimation under multipath signals )

搜集并整理关于MVDR CBF 对角加载技术的程序，对于入门者很有帮助(Collecting and sorting on MVDR CBF diagonal loading technology program, very helpful for beginners)

多机电力系统仿真及其潮流计算，是路径规划的实用方法，各种资源分配算法实现，对于初学者具有参考意义，是一种双隐层反向传播神经网络，考虑雨衰 阴影 和多径影响。

用matlab编写的遗传算法用来快速求解目标函数的极值！欢迎你下载学习！(Prepared using matlab genetic algorithm used to solve the objective function Express Extreme! Welcome to download you study!)

这是一个利用自相关函数以及中心肖波算法求取基音频率的程序，其基于MATLAB。(it is a program about get pitch.it based on MATLAB)

本文介绍一种波导双模双带滤波器的设计方法。(This paper presents a new class of dual-mode dualband filters in which each polarization is dedicated to a selected band. The equivalent circuit is a parallel combination of two inline networks that represent each polarization. A transmission zero is generated between the two bands by properly adjusting the relative orientations of the input and output coupling apertures.)

说明：  使用matlab的simulink进行仿真GMSK，并画出仿真图。(GMSK is simulated with Simulink of matlab, and the simulation diagram is drawn.)

HILBERTAVERAGE PITCH CALCULATION

说明：  复杂环境中的微弱目标（如隐身飞机、被地/海/城市杂波淹没的目标等）探测 问题是现代雷达面临的严峻挑战。 与传统的先检测后跟踪（ DBT） 技术相比， 检 测前跟踪（TBD）技术是一种新兴的先进信号处理技术，它通过多帧回波数据积 累和联合处理，可以显著提高雷达的微弱目标检测跟踪性能， 是国际雷达界研究 的前沿热点。 TBD 作为一项正在发展中的新技术，还需要解决一些新的技术问题， 例如， 多目标跟踪维数灾难、临近目标相互干扰等，以及处理难度增加和运算量 增大带来的新问题。(The detection and tracking of low observable targets (e.g. stealth targets, targets buried in strong clutter) in complex environment are great challenges for modern radar systems. Different from the traditional detect-before-track (DBT) methods, Track-before-detect (TBD) is a novel and efficient signal processing method which is proposed in recent years to detect low observable targets, and has got much attention internationally in radar research area. By jointly processing several data frames, TBD is able to produce more reliable detection and tracking results. As a developing new technique, TBD has its own problems and challenges. For example, existing studies on)