Many of you were onto me immediately last week. I asked you to estimate the frequency of the sampled cosine signal below, and readers quickly chimed in to guess that this question was really a teaser about aliasing.

As it happened, I started with a 1 rad/second cosine, , :

t = linspace(-2.3*pi, 2.3*pi, 800);
alpha = 1;
x_c = cos(alpha * t);
plot(t, x_c, 'k')

And I sampled that signal to get with so that .

T = 1;
n = -7:7;
nt = n * T;
x = cos(alpha*n*T);
hold on
plot(nt, x, 'ok')
hold off

But I could have started with a completely different frequency and still ended up with exactly the same samples. For example, let's see what happens when we try :

alpha_2 = 2*pi - 1;
x2_c = cos(alpha_2 * t);
plot(t, x2_c, 'k')

x2 = cos(alpha_2 * n * T);
hold on
plot(nt, x2, 'ok')
hold off

The two sets of samples are the same (within floating-point round-off error):

max(abs(x - x2))

ans =

  1.1657e-015

Let's look at this phenomenon in terms of the relationship between the continuous-time Fourier transform and the discrete-time Fourier transform (DTFT). Below is the continuous-time Fourier transform of .

The DTFT is a collection of copies of the continuous-time Fourier transform, spaced apart by the sampling frequency, and with the frequency axis scaled so that the sampling frequency becomes . Here's the DTFT of .

Now has a higher frequency as you can see in the plot of its continuous-time Fourier transform:

But when you make a bunch of copies of spaced apart by the sampling frequency, you find that the DTFT of is exactly the same as the DTFT of .

Usually only a single period of the DTFT is plotted:

In other words, when you use a sampling rate of , the frequencies 1 and are indistinguishable. This is called aliasing. In general, the continuous-time frequency is indistinguishable from any other frequency of the form , where is an integer.

So far we've talked about the continuous-time Fourier transform, the discrete-time Fourier transform, their relationship, and a little bit about aliasing. Next time we'll bring the discrete Fourier transform (DFT) into the discussion. That's what the MATLAB function fft actually computes.

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Published with MATLAB® 7.9