Cross Correlation


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. It has applications in pattern recognition, single particle analysis, electron tomography, averaging, cryptanalysis, and neurophysiology. The cross-correlation is similar in nature to the convolution of two functions.

Explanation:

Consider two finite length sequences, say x(n) and y(n). The cross correlation of x(n) and y(n) can be obtained by simply performing convolution between x(n) and y(-n) i.e

Rxy(n) = x(n)*y(-n)



The step-by-step values for the provided input is shown at the bottom of the page:

light_mode dark_mode

Enter the first sequence :



Enter the second sequence :







x(n) :




y(n) :




Rxy(n) :


x(n) and y(n) :


Add the below sequences :


Rxy(n) :