C-C-Method

Radar System Performance Modeling, Second Edition 的光盘源码(Radar System Performance Modeling, Second Edition )

Vertex Component Analysis: A Fast Algorithm to Unmix Hyperspectral Data(This paper presents a new method for unsupervised endmember extraction from hyperspectral data, termed vertex component analysis (VCA).)

sins捷联惯导卡尔曼滤波程序，用的是松组合组合模式，适用于初学者理解卡尔曼滤波(Sins Kalman filtering process, with a loose combination)

bisection , Bi-section

主要用于实现基于TOA/AOA/RSS/QRSS的二维定位算法，用matlab程序实现，内附有注释(Mainly used to implement based on TOA/AOA/RSS/QRSS two-dimensional positioning algorithm, using matlab program, enclosing a Comment)

不完全微分PID算法Matlab仿真程序。适用于控制理论领域。(Incomplete differential PID algorithm Matlab simulation program. Applicable to the field of control theory.)

递归核最小二乘算法，来至MIT大学的wingate教授，含6个源码，有实例！(dict_init.m- Part of the dictionary implementation used by KRLS algorithm. Can stand alone. dict.m- Part of the dictionary implementation used by the KRLS algorithm. Can stand alone. krls_init.m- Kernel recursive least squares initializer. krls.m- Main KRLS function. Repeatedly called with new data points. krls_query.m- Query the resulting estimator. krls_example.m- An example script showing the different parts. )

振动信号预处理方法---五点滑动平均法平滑处理,matlab程序(Vibration signal preprocessing method--- five-point moving average smoothing, matlab program)

Matlab通信仿真原理，中文的，是一本入门级别的，写的很详细，也有很多详细的例子(Matlab Communication simulation theory, in Chinese, is an entry-level, written in great detail, there are many detailed examples)

说明：  共轭梯度法（Conjugate Gradient）是介于最速下降法与牛顿法之间的一个方法，它仅需利用一阶导数信息，但克服了最速下降法收敛慢的缺点，又避免了牛顿法需要存储和计算Hesse矩阵并求逆的缺点，共轭梯度法不仅是解决大型线性方程组最有用的方法之一，也是解大型非线性最优化最有效的算法之一。 在各种优化算法中，共轭梯度法是非常重要的一种。其优点是所需存储量小，具有步收敛性，稳定性高，而且不需要任何外来参数。(The Conjugate Gradient method is a method between the steepest descent method and the Newton method. It only needs to use the first derivative information, but overcomes the shortcomings of the steepest descent method and avoids the need for the Newton method to store And the calculation of Hesse matrix and the inverse of the shortcomings, conjugate gradient method is not only to solve large-scale linear equations one of the most useful methods, but also solution for large-scale nonlinear optimization of one of the most effective algorithm. In a variety of optimization algorithms, conjugate gradient method is a very important one. The advantage is that the required storage capacity is small, with step convergence, high stability, and does not require any external parameters.)