源码文件
%PARZENML Optimum smoothing parameter in Parzen density estimation. % Soft label version % % H = PARZENML(A,FID) % % IN…
DOA+estimation 源代码
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源码文件
%PARZENML Optimum smoothing parameter in Parzen density estimation. % % H = PARZENML(A,FID) % % INPUT % A input dataset %…
源码文件
%GPS Lecture 5 %Date 12-Nov-1999 % %DD_COV Computes the covariance matrix for double differenced % observations betw…
源码文件
%GPS Toolbox %Version 1.0 17-Oct-1997 % %Directory: ash_base % %ASH_BASE Estimation of ambiguities by sequential least square…
源码文件
%PARZENML Optimum smoothing parameter in Parzen density estimation. % % H = PARZENML(A,FID) % % INPUT % A input dataset %…
源码文件
%PARZENML Optimum smoothing parameter in Parzen density estimation. % Soft label version % % H = PARZENML(A,FID) % % IN…
源码文件
clear all; close all; clc; % plot LS channel estimation figure; BER_AWGN0 = [0.183034179687500,0.0907099609375000,0.02642…
源码文件
clear all; close all; clc; % plot LS channel estimation figure; BER_AWGN0 = [0.183034179687500,0.0907099609375000,0.02642…
源码文件
%Discrete Kalman filter %x=Ax+B(u+w(k)); %y=Cx+D+v(k) function [u]=kalman(u1,u2,u3) persistent A B C D Q R P x yv=u2; if u…
源码文件
%Discrete Kalman filter %x=Ax+B(u+w(k)); %y=Cx+D+v(k) function [u]=kalman(u1,u2,u3) persistent A B C D Q R P x yv=u2; if u…