ComplementFilter

随机过程中的泊松过程，使用MATLAB模拟的(Random process Poisson process, using the MATLAB simulation)

说明：  循环理论相关MATLAB源代码：高斯信号的产生及画图(Cyclostationary related)

产生随机信号观测样本，不同信噪比和归一化频率。利用基于FFT的自相关函数快速计算方法估计其自相关函数。(Generate random signal observation samples, different signal to noise ratio and normalized frequency.Based on FFT calculation method of the autocorrelation function to quickly estimate the autocorrelation function.)

利用cst远场数据在matlab画出波瓣图(Use cst far field data lobe pattern shown in matlab)

说明：  主要功能： 1.完成传感器对目标状态的kalman滤波估计； 2.对传感器的状态估计进行SCC和CI融合； 3.画出位置及速度的估计和融合误差曲线、真实航迹及融合后航迹、K=1时刻的协方差椭圆(Main functions: 1. The Kalman filter estimation of the target state is completed; 2. The state estimation of sensors is fused by SCC and CI; 3. Draw the error curve of position and velocity estimation and fusion, real track and fusion track, covariance ellipse at k = 1)

OFDM中FPGA部分，随机化，信道编码，交织，这里主要介绍了利用RS编码实现的方法(FPGA portion of the OFDM system, randomization, channel coding, interleaving, introduces the use of RS coding method)

David Dunavant, High Degree Efficient Symmetrical Gaussian Quadrature Rules for the Triangle, International Journal for Numerical Methods in Engineering, Volume 21, 1985, pages 1129-1148.

m文件，有助于理解emd算法。有移植价值，初学emd必备(m file, helps to understand emd algorithm. Transplant value, emd essential beginner)

均匀圆阵仿真:m=8 阵元数 azimuth=[-55 0 40] 方位角 p=length(azimuth) 信号源个数 snap=1000 最大快拍数 drl=0.5 圆阵半径与波长之比 snr=15 信噪比dB 流程：1、构造信号2、构造噪声阵列流型矩阵3、观测信号4、观测信号协方差矩阵5、求解特征值与特征向量6、显示图谱。(The uniform circular array simulation: m = 8 element azimuth = [-55 0 40] azimuth p = length (azimuth) number of source snap = 1000 maximum number of snapshots drl = 0.5 ratio of the radius of the circular array wavelength snr = 15 SNR dB process: 1, constructed signal structure noise array manifold matrix 3, the observed signals observed signals covariance matrix 5 eigenvalue and eigenvector 6, display the map.)

广义的互相提取时延算法,此程序是基于matlab的源程序,适用于非高斯信号和高斯白噪声的情况(Generalized mutual delay extraction algorithms, this procedure is based on the matlab source, applicable to non-Gaussian signal and Gaussian white noise situation)