背景

????????在處理陣列信號的時候，為了獲得空間信號維度的相關性，以估計目標的信息。故使用協方差矩陣能夠獲得這些，因為協方差矩陣是每一維度下（也就是陣元）信號的相關性。當兩個維度相關時，信號的協方差也是最大的。

源代碼

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % 陣列信號基礎（協方差矩陣） %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% clc; close all; clear all;%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % 信源 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % 隨機產生10*3維的整數矩陣作為樣本集，8為樣本的個數，3為樣本的維數 s=fix(rand(8,3)*10); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % 法一 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% cov(s) % 計算協方差矩陣%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % 法二 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% dim1=s(:,1); %得到第1維的數據 dim2=s(:,2); %得到第2維的數據 dim3=s(:,3); %得到第3維的數據 % size(mysample,1) %第一維的大小 cov12=sum((dim1-mean(dim1)).*(dim2-mean(dim2)))/(size(s,1)-1); %計算dim1和dim2 的協方差 cov21=cov12; cov13=sum((dim1-mean(dim1)).*(dim3-mean(dim3)))/(size(s,1)-1); %計算dim1和dim3 的協方差 cov31=cov13; cov23=sum((dim2-mean(dim2)).*(dim3-mean(dim3)))/(size(s,1)-1); %計算dim2和dim3 的協方差 cov32=cov23; % 協方差矩陣對角線上的元素就是各個維度的方差，下面計算 var1=std(dim1)^2; var2=std(dim2)^2; var3=std(dim3)^2; answer=[var1,cov12,cov13;cov21,var2,cov23;cov31,cov32,var3]%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % 結論 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % 法一直接利用函數得到，法二通過推導得到，有利于理解協方差矩陣本質 % 三維協方差矩陣 % cov(x,x) cov(x,y) cov(x,z) % cov(y,x) cov(y,y) cov(y,z) % cov(z,x) cov(z,y) cov(z,z)

仿真結果

總結

以上是生活随笔為你收集整理的阵列信号处理基础（一、协方差矩阵）的全部內容，希望文章能夠幫你解決所遇到的問題。

如果覺得生活随笔網站內容還不錯，歡迎將生活随笔推薦給好友。