matlab-program-resource

是MATLAB关于cc++的使用和应用（英文）。(Is MATLAB on cc++ The use and application (in English).)

说明：  适合Matlab的初学者，主要讲了Matlab的一些基础知识，都很基本的。(Matlab for the beginner, the main speech of some basic knowledge of Matlab, very basic.)

This function is useful to make a dateaxis adjustable

自动控制原理，对原系统进行PID校正，观测BODE图以及时域波形的简要程序。(Automatic control theory, PID correction of the original system, a brief observation program BODE diagram and time-domain waveform.)

该文档里面包含了MATLAB里常用的命令及例子，介绍的非常详细。(The document which contains a MATLAB command that is commonly used and examples described in great detail.)

Nearest neighbors is a semi deconvolution algorithm. I m a newbie in matlab but I hope this helps somebody :)

位同步仿真，有仿真数据的输入，以及转换，可以输入到verilog中仿真(Bit synchronous simulation, simulation data input, and conversion, can be entered into the simulation verilog)

黄锷论文中EMD和HHT的源程序，已经打包压缩,解压后可以使用(Wong Ngok paper on EMD and HHT source, has been packaged compression, decompression can be used after)

用MIMO系统的空时编码仿真,matlab实现,性能对比分析(With space-time coding MIMO system simulation, matlab implementation, performance, comparative analysis)

说明：  EKF仅仅利用了非线性函数Taylor展开式的一阶偏导部分（忽略高阶项），常常导致在状态的后验分布的估计上产生较大的误差，影响滤波算法的性能，从而影响整个跟踪系统的性能。最近，在自适应滤波领域又出现了新的算法——无味变换Kalman滤波器（Unscented Kalman Filter-UKF）。UKF的思想不同于EKF滤波，它通过设计少量的σ点，由σ点经由非线性函数的传播，计算出随机向量一、二阶统计特性的传播。因此它比EKF滤波能更好地迫近状态方程的非线性特性，从而比EKF滤波具有更高的估计精度。 (EKF only uses non-linear function of the first-order Taylor expansion of some partial derivatives (ignoring higher order terms), often leading to the posterior distribution of the state estimates to generate large errors affect the performance of filtering algorithms, which affect the whole tracking system performance. Recently, the field of adaptive filtering algorithms and the emergence of new- and tasteless transform Kalman filter (Unscented Kalman Filter-UKF). EKF UKF filter is different from the idea that it points through the design of a small amount of σ by σ point spread through the nonlinear function to calculate the random vector first and second order statistical properties of the transmission. Therefore it is better than the EKF filter nonlinear characteristics equation of state approach, which is more than the EKF filter estimation accuracy.)