Basis for comparison of random signal analysis and
2016-08-23
0 0 0
no vote
Other
Earn points
Example 3 correlation functions of random signals, from X2 to see the composition of the signal, but from the autocorrelation function of the X2 there is a signal at the origin, we can be sure that the signal in the noise
Example 4 illustrates the x,A2 of the routine determines the proportion of white noise, more pollution the more serious of the original signal, but the autocorrelation function of signal will be more obvious
Routines and routine 5 4 similar
Example 7 is the periodogram for spectral estimation, figure (2) is their definition of periodogram formula figure (3) is a function calls Matlab periodogram
Example 9 is called Welch power spectrum estimation methods
Example 11 is the maximum entropy method
Example 4 illustrates the x,A2 of the routine determines the proportion of white noise, more pollution the more serious of the original signal, but the autocorrelation function of signal will be more obvious
Routines and routine 5 4 similar
Example 7 is the periodogram for spectral estimation, figure (2) is their definition of periodogram formula figure (3) is a function calls Matlab periodogram
Example 9 is called Welch power spectrum estimation methods
Example 11 is the maximum entropy method
matlab
txt
基础
分析
比较
随机
注释
说明
信号
相关
重要
Related Source Codes
GMSK Linear Receiver
0
0
no vote
NSGA-II algorithm
0
0
no vote
NSGA-III multi-objective optimization algorithm
0
0
no vote
Compressed sensing example
0
0
no vote
CFAR detector example
0
0
no vote
No comment