Limited cross-correlation for multiple signals

1. What is cross-correlation of signals?
2. How do you find the cross-correlation of two signals?
3. Why do we need correlation to perform on two signals?
4. When can cross-correlation be considered as auto correlation?

What is cross-correlation of signals?

In signal processing, cross-correlation is a measure of similarity of two series as a function of the displacement of one relative to the other. This is also known as a sliding dot product or sliding inner-product. It is commonly used for searching a long signal for a shorter, known feature.

How do you find the cross-correlation of two signals?

To detect a level of correlation between two signals we use cross-correlation. It is calculated simply by multiplying and summing two-time series together. In the following example, graphs A and B are cross-correlated but graph C is not correlated to either.

Why do we need correlation to perform on two signals?

That is, correlation between signals indicates the measure up to which the given signal resembles another signal. In other words, if we want to know how much similarity exists between the signals 1 and 2, then we need to find out the correlation of Signal 1 with respect to Signal 2 or vice versa.

When can cross-correlation be considered as auto correlation?

Cross correlation happens when two different sequences are correlated. Autocorrelation is the correlation between two of the same sequences. In other words, you correlate a signal with itself.