Cross-correlation or cross-covariance of non-zero mean signals

1. Is cross-correlation the same as covariance?
2. What is cross-correlation in signals?
3. How do you interpret cross-covariance?
4. How do you find the cross-correlation of two signals?

Is cross-correlation the same as covariance?

In signal processing, the cross-covariance is often called cross-correlation and is a measure of similarity of two signals, commonly used to find features in an unknown signal by comparing it to a known one.

What is cross-correlation in 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 interpret cross-covariance?

If cov(X, Y) is positive, then larger values of X are associated with larger values of Y and smaller values of X are associated with smaller values of Y. If the covariance is negative, the opposite holds: small Xs are associated with larger Ys and vice versa.

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.