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By : misanov
Date : November 22 2020, 10:40 AM
To fix the issue you can do The cross-correlation of two complex function equals the convolution of one function and the complex conjugate of the other:
Cross correlation and convolution code :
``````convolve(my.vector1, my.vector2)
``````
``````which.max(convolve(my.vector1,my.vector2))
`````` ## Cross Correlation with Discrete signals

By : CZ云遥
Date : March 29 2020, 07:55 AM
Hope that helps Just do the regular cross correlation. Afterwards set the lags vector accordingly. ## Cross-Correlation of two signals

By : SkyWaiting
Date : March 29 2020, 07:55 AM
To fix the issue you can do You seem to have a slight misunderstanding of how cross-correlation works. Cross-correlation takes one signal, and compares it with shifted versions of another signal. If you recall, the (unnormalized) cross-correlation of two signals is defined as:
code :
``````s = [0 0 0 0 1 1 1 1 1 0 0 0 0]
h = [1 1 1 1 1 0 0 0 0 0 0 0 0]
``````
``````s ** h = (0)(1) + (0)(1) + (0)(1) + (0)(1) + (1)(1) + (0)(1) + (0)(1) + (0)(1) + (0)(1)
= 1
``````
``````s = [0 0 0 0 1 1 1 1 1 0 0 0 0]
h = [0 1 1 1 1 1 0 0 0 0 0 0 0]
``````
``````s ** h = (0)(1) + (0)(1) + (0)(1) + (0)(1) + (1)(1) + (1)(1) + (0)(1) + (0)(1) + (0)(1)
= 2
``````
``````s = [0 0 0 0 1 1 1 1 1 0 0 0 0]
h = [0 0 0 0 1 1 1 1 1 0 0 0 0]
``````
``````s = [0 0 0 0 1 1 1 1 1 0 0 0 0]
h = [0 0 0 0 0 1 1 1 1 1 0 0 0]
``````
``````s = [0 0 0 0 1 1 1 1 1 0 0 0 0]
h = [0 0 0 0 0 0 0 0 1 1 1 1 1]
`````` ## Cross Correlation with signals of different length in MATLAB

By : plrnvld
Date : March 29 2020, 07:55 AM
this will help I have 2 signals of different lengths where the shorter signal is the same as the longer n samples shifted. I want to find the maximum normalized cross-correlation between these two signals. Since it is normalized should give 1. The xcorr function lags vary from -441 to 441 samples. , Suppose x and y are the shorter and longer signals you have.
code :
``````nx = length(x);
ny = length(y);
cc = nan(1,ny-nx+1);
for ii = 0 : ny-nx
id = (1:nx) + ii;
cc(ii+1) = sum(x.*y(id))/(sqrt(sum(x.^2)*sum(y(id).^2)));
end
[ccmx,idmx] = max(cc);
`````` ## Normalizing cross-correlation to [-1,1] for different length signals

By : Megha Jain
Date : March 29 2020, 07:55 AM
I wish did fix the issue. Following the documentation https://it.mathworks.com/help/signal/ref/xcorr.html
There is the Normalization option on xcorr function
code :
``````[acor,lag] = xcorr(signal1,signal2,'coeff');
`````` ## Cross correlation / similarity of signals - calculate time lag

By : Nickolass
Date : March 29 2020, 07:55 AM
I think the issue was by ths following , One possible solution to your problem is Mean Squared Error (MSE). Given two signals a and b of same dimensions, MSE is the average value of the element-wise squares of the difference between a and b. The code would look like follows (based on this):
code :
``````import numpy as np
import matplotlib.pyplot as plt

a = np.array([10, 20, 10, 30, 20, 10, 28, 22, 10])
b = np.array([28, 22])
mse = np.ndarray((len(a) - len(b) + 1))

for i in range(c.size):
mse[i] = np.square(np.subtract(a[i:i+len(b)],b)).mean()

print(mse.argmin())
plt.plot(mse)
`````` 