Matlab_examples

It s a useful thing helping you to pragramming the finite element with matlab.

提出了一种求解电磁场有限元 边界元混合法所生成的线性方程组的有效方法 ———内观 法结合多波前法.由于该线性方程组的系数是一个部分稀疏部分满填充的矩阵,为了加速求解,应用内观法将系数矩阵分为 2块,一块是有限元法形成的稀疏矩阵,另一块是边界元法生成的满阵,然后用多波前法求解稀疏矩阵方程,用高斯 约当消去法解满阵方程. 采用该方法,计算了二维多层介质柱体的雷达散射截面.计算结果表明,该方法的计算效率远远高于传统的高斯法.(Proposed for solving mixed finite element boundary element linear equations generated by an effective method of Vipassana--- a combination of multi wave front method. Since the coefficients of linear equations is a part of the sparsely populated part of the full matrix, in order to speed up the solution, application of Vipassana coefficient matrix is ​ ​ divided into two, one is the finite element method for the formation of the sparse matrix, the other is the boundary element method generates the full array, and then use the multifrontal method for solving sparse matrix equation Gauss Jordan elimination method with full matrix equation solution. use this method to calculate the two-dimensional multi-layer dielectric cylinder of the radar cross section. The results show the computational efficiency of this method is much higher than the traditional Gaussian law.)

说明：  广义互相关测信号时延的matlab代码，采用TDOA方法，其中延时计算采用GCC方法(Matlab code for time delay measurement of generalized cross-correlation signals)

Simulation of the real time traffic signals

可以让用户通过自己定义的变量来调入常用的Kernel(Allows users to adopt its own definition of the variables commonly used redeployed Kernel)

simple power line channel model

小波包分析提取振动信号中的特征频率，D-S证据理论数据融合，能量熵的计算。( Wavelet packet analysis to extract vibration signal characteristic frequency, D-S evidence theory data fusion, Energy entropy calculation.)

通过广播星历和精密星历实现GNSS数据的快速解算(The fast calculation of GNSS data is realized by broadcast ephemeris and precise ephemeris)

基于SVD海杂波抑制算法 文件中包含源程序！(a clutter suppression scheme based on SVD including the original code!)

The standard optimum Kalman filter demands complete knowledge of the system parameters, the input forcing functions, and the noise statistics. Several adaptive methods have already been devised to obtain the unknown information using the measurements and the filter residuals.(The optimum standard Kalman filter demand 's complete knowledge of the system parameters. the input forcing functions. and the noise statistics. Several adaptive met hods have already been devised to obtain the unk nown information using the measurements and th e filter residuals.)