This document describes an experiment to write Matlab programs for cross correlation and auto correlation of signals. It provides theory on correlation, defines cross correlation and auto correlation, and includes Matlab code examples to calculate both. The code demonstrates taking input signals, performing the correlations, and plotting the output correlated signals. The overall goal is to learn how to implement correlation in Matlab.

1. Experiment No: 02
Experiment Name: Write a simple Matlab program to perform correlation of two signals:
1) Cross correlation
2) Auto correlation
Objectives:
 To learn about Cross and Auto Correlation.
 To learn about how to perform correlation using Matlab code.
Theory:
Correlation:
Correlation is a measure f similarity between two signals. The general formula for correlation is
∫ 𝒙𝟏(𝒕)𝒙𝟐(𝒕 − 𝝉)𝒅𝒕
∞
−∞
Cross correlation:
Cross correlation is a measure of similarity of two series as a function of the lag of one relative to the
other.
Auto correlation:
Autocorrelation is a mathematical representation of the degree of similarity between a given time
series and a lagged version of itself over successive time intervals
Auto Correlation Code:
clear all;
close all;
clc;
x=[1 2 3]
y=input('Initial position ');
yy=input('Initial position of 2nd signal ');
k=[];
m=length(x);
p=(y-1)*-1;
d=1;
for i= p:1:(m-y)
k (d)=i;
d=d+1;
k
end
figure (1)
subplot(3,1,1);
stem(k,x,'g','LineWidth',3);
title('Discrete Signal represntation ');
xlabel('Time(t) ');
ylabel('x(n)');
xx=[3 2 1]

2. kk=[];
mm=length(xx);
pp=(yy-1)*-1;
dd=1;
for ii= pp:1:(mm-yy)
kk (dd)=ii;
dd=dd+1;
kk
end
figure (1)
subplot(3,1,2);
r=[];
r=xcorr(xx,x);
stem(kk,xx,'g','LineWidth',3);
lx=m+mm-1;
rx=p+pp;
cx=[];
g=1;
for i=1:1:lx
cx(g)=rx;
rx=rx+1;
g=g+1;
end
cx
g
figure (2);
stem(cx,r,'g','LineWidth',3);
Input Signals:
1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 2.8 3
0
2
4
Discrete Signal represntation
Time(t)
x(n)
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
0
2
4

3. Output Signal (Auto Correlated Signal):
Cross Correlation code
clear all;
close all;
m = input('length');
for i=1:m
x(i)=input('value');
end
p=input('initial');
n=input('length');
for i=1:m
y(i)=input('value');
end
q=input('initial');
l=m+n-1;
pp=-(p-1);
qq=-(q-1);
lower=pp+qq;
for i=1:1:1
rr(i)=lower;
lower=lower+1;
end
for i=1:1:1
if i<=m
x(i)=x(i);
else
1 1.5 2 2.5 3 3.5 4 4.5 5
0
2
4
6
8
10
12

4. x(i)=0;
end
end
for i=1:1:1
if i<=n
y(i)=y(i);
else
y(i)=0;
end
end
for j=1:1:1
p1=0;
q1=j;
for i=1:1:j
p1=p1+x(q1)*y(i);
q1=q1-1;
end
r(i)=p1;
end
stem(rr,r)
Input Output
Length 4
value1
value2
value3
value4
initial 1
length 4
value4
value3
value2
value1
initial 4
Comments: From this lab we know about Cross and Auto correlation and know how to implement these
correlations into Matlab code. This knowledge will help us in further lab work. This lab helps us to clear
our idea about correlation
-4 -3.8 -3.6 -3.4 -3.2 -3 -2.8 -2.6 -2.4 -2.2 -2
0
0.5
1
1.5
2
2.5
3
3.5
4