代码搜索：ESTIMATION
找到约 3,786 项符合「ESTIMATION」的源代码
代码结果 3,786
www.eeworm.com/read/398034/8009023
m sa_ex7_7.m
% Linear predictive AOA estimation for a M = 6 element array with noise variance = .1
% allow transpose(u3) = [0 0 1 0 0 0]
M=6;
sig2=.1;
th1=-5*pi/180;
th2=5*pi/180;
a1=[1];
a2=[1];
a=[1];
www.eeworm.com/read/397102/8068249
m parzenml.m
%PARZENML Optimum smoothing parameter in Parzen density estimation.
%
% h = parzenml(A)
%
% Maximum likelihood estimation for the smoothing parameter in the
% Parzen denstity estimation of the dat
www.eeworm.com/read/241192/13164309
m potter.m
function [xout,Cout] = potter(z,R,H,xin,Cin)
%
% James H. Potter's square root filtering algorithm
% for the observational update of a Cholesky factor
% of the covariance matrix of state estimatio
www.eeworm.com/read/240226/13229961
m rake.m
%% Rake receiver of CDMA
%% may need call lcode.mat to load PN sequence (lcode)
%
% Copyright: Xiaohua(Edward) Li, Assistant Professor
% Department of Electrical and Computer Engineerin
www.eeworm.com/read/320444/13426912
m rakereceverofcdma.m
%% Rake receiver of CDMA
%% may need call lcode.mat to load PN sequence (lcode)
%
% Copyright: Xiaohua(Edward) Li, Assistant Professor
% Department of Electrical and Computer Engineerin
www.eeworm.com/read/319567/13448669
txt read me.txt
to run program run main44
files included
pframecal: will do processing regarding P frames
bframecal: will do processing regarding B frames
Iframecal: will do processing regarding I frames
GOP: wi
www.eeworm.com/read/318840/13471277
m potter.m
function [xout,Cout] = potter(z,R,H,xin,Cin)
%
% James H. Potter's square root filtering algorithm
% for the observational update of a Cholesky factor
% of the covariance matrix of state estimatio
www.eeworm.com/read/317326/13505871
m sa_ex7_6.m
% Capon AOA estimation for a M = 6 element array with noise variance = .1
M=6;
sig2=.1;
th1=-5*pi/180;
th2=5*pi/180;
a1=[1];
a2=[1];
a=[1];
for i=2:M
a1=[a1 exp(-1j*i*pi*sin(th1))];
www.eeworm.com/read/317326/13505884
m sa_ex7_me.m
% Maximum Entropy AOA estimation for a M = 6 element array with noise variance = .1
%
tic
M=6;
sig2=.1;
th1=-5*pi/180;
th2=5*pi/180;
a1=[1];
a2=[1];
a=[1];
u3=[0 0 1 0 0 0];
%for i=2:M
%
www.eeworm.com/read/317326/13505887
m sa_ex7_7.m
% Linear predictive AOA estimation for a M = 6 element array with noise variance = .1
% allow transpose(u3) = [0 0 1 0 0 0]
M=6;
sig2=.1;
th1=-5*pi/180;
th2=5*pi/180;
a1=[1];
a2=[1];
a=[1];