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c = cconv(a,b,n)
Circular convolution is used to convolve two discrete Fourier transform (DFT) sequences. For very long sequences, circular convolution may be faster than linear convolution.
c = cconv(a,b,n) circularly convolves vectors a and b. n is the length of the resulting vector. If you omit n, it defaults to length(a)+length(B)-1. When n = length(a)+length(B)-1, the circular convolution is equivalent to the linear convolution computed with conv. You can also use cconv to compute the circular cross-correlation of two sequences (see the example below).
The following example calculates a modulo-4 circular convolution.
a = [2 1 2 1];
b = [1 2 3 4];
c = cconv(a,b,4)
c =
14 16 14 16 The following example compares a circular correlation, where n uses the default value, and a linear convolution. The resulting norm is a value that is virtually zero, which shows that the two convolutions produce virtually the same result.
a = [1 2 -1 1]; b = [1 1 2 1 2 2 1 1]; c = cconv(a,b) % Circular convolution cref = conv(a,b) % Linear convolution norm(c-cref) ans = 9.7422e-016
The following example uses cconv to compute the circular cross-correlation of two sequences. The result is compared to the cross-correlation computed using xcorr.
a = [1 2 2 1]+i; b = [1 3 4 1]-2*i; c = cconv(a,conj(fliplr(b)),7) % Compute using cconv cref = xcorr(a,b) % Compute using xcorr c = Columns 1 through 5 -1.0000 + 3.0000i 2.0000 +11.0000i 7.0000 +18.0000i 8.0000 +21.0000i 6.0000 +18.0000i Columns 6 through 7 1.0000 +10.0000i -1.0000 + 3.0000i cref = Columns 1 through 5 -1.0000 + 3.0000i 2.0000 +11.0000i 7.0000 +18.0000i 8.0000 +21.0000i 6.0000 +18.0000i Columns 6 through 7 1.0000 +10.0000i -1.0000 + 3.0000i
[1] Orfanidis, S.J., Introduction to Signal Processing, Englewood Cliffs, NJ: Prentice-Hall, Inc., 1996. pp. 524–529.
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