Kalman-filtering

MATLAB关于绘图的设置与调整，这里主要举了一个列子说明，包括xy轴的比例、字号、线宽等(related about setting and adjustment of the graphic in matlab)

fit_rayleigh_pdf - Non Linear Least Squares fit of the Rayleigh distribution. given the samples of the histogram of the samples, finds the distribution parameter that fits the histogram samples.fits data to the probability of the form: p(r)=r*exp(-r^2/(2*s))/s with parameter: s format:result = fit_rayleigh_pdf( x,y,W,hAx ) input: y - vector, samples of the histogram to be fitted x - vector, position of the samples of the histogram (i.e. y = f(x,a)) W - matrix or scalar, a square weighting matrix of the size NxN where N = length(y), or 0 to indicate no weighting is needed. hAx - handle of an axis, on which the fitted distribution is plotted output: result - structure with the fields s - fitted parameter VAR - variance of the estimation type- weighted LS or not weighted LS iter- number of iteration for the solution

simuale data extraction of wave heart sound and classify ii

ookd是相位调制的一种代码程序，可以看到调制效果。(ookd)

说明：  经典的MATLAB实用例子，可使初学者较快掌握MATLAB的一些基本函数及用法(The classic example of MATLAB utility will enable the beginner to master MATLAB faster some of the basic functions and usage of)

说明：  两种对二阶系统进行参数辨识的最小二乘算法，一种可以直接得到输入输出之间的传递函数，一种可以解出线性的差分方程各系数，再反解得到传递函数。附带的一个二阶系统的模型，可以自行下载导入输入输出数据进行试验。(Two kinds of least square algorithm for parameter identification of second-order system, one can directly get the transfer function between input and output, the other can solve the coefficients of linear difference equation, and then get the transfer function by inverse solution. The attached model of a second-order system can download the input and output data for test.)

关于计算机模拟的matlab实验(Matlab on the computer simulation experiment)

计算图像PSNR，非常高效的源码，具有一定的参考价值。(Calculated image PSNR, very efficient source, with some reference value.)

The design of Kalman filter for estimation of state

Knowledge of the process noise covariance matrix is essential for the application of Kalman filtering. However, it is usually a difficult task to obtain an explicit expression of for large time varying systems. This paper looks at an adaptive Kalman filter method for dynamic harmonic state estimation and harmonic injection tracking.(Knowledge of the process noise covariance matrix is essential for the application of Kalm an filtering. However, it is usually a difficult task to obtain an expli cit for large expression of time varying system s. This paper looks at an adaptive Kalman filter method for dynamic estimation a harmonic state nd harmonic injection tracking.)