The discrete Fourier transform, or DFT, is the primary tool of digital signal processing. The foundation of the Signal Processing Toolbox™ product is the fast Fourier transform (FFT), a method for computing the DFT with reduced execution time. Many of the toolbox functions (including z-domain frequency response, spectrum and cepstrum analysis, and some filter design and implementation functions) incorporate the FFT.
The MATLAB^{®} environment provides the functions fft
and ifft
to compute the discrete Fourier transform and its inverse,
respectively. For the input sequence x and its
transformed version X (the discrete-time Fourier
transform at equally spaced frequencies around the unit circle), the
two functions implement the relationships
$$X(k+1)={\displaystyle \sum _{n=0}^{N-1}x}(n+1){W}_{N}^{kn},$$
and
$$x(n+1)=\frac{1}{N}{\displaystyle \sum _{k=0}^{N-1}X}(k+1){W}_{N}^{-kn}.$$
In these equations, the series subscripts begin with 1 instead of 0 because of the MATLAB vector indexing scheme, and
$${W}_{N}={e}^{-j2\pi /N}.$$
Note
The MATLAB convention is to use a negative j for
the |
fft
, with a single input argument, x
,
computes the DFT of the input vector or matrix. If x
is
a vector, fft
computes the DFT of the vector; if x
is
a rectangular array, fft
computes the DFT of each
array column.
For example, create a time vector and signal:
t = 0:1/100:10-1/100; % Time vector x = sin(2*pi*15*t) + sin(2*pi*40*t); % Signal
y = fft(x); % Compute DFT of x m = abs(y); % Magnitude p = unwrap(angle(y)); % Phase
To plot the magnitude and phase, type the following commands:
f = (0:length(y)-1)*100/length(y); % Frequency vector subplot(2,1,1) plot(f,m) title('Magnitude') ax = gca; ax.XTick = [15 40 60 85]; subplot(2,1,2) plot(f,p*180/pi) title('Phase') ax = gca; ax.XTick = [15 40 60 85];
A second argument to fft
specifies a number
of points n
for the transform, representing DFT
length:
y = fft(x,n);
In this case, fft
pads the input sequence
with zeros if it is shorter than n
, or truncates
the sequence if it is longer than n
. If n
is
not specified, it defaults to the length of the input sequence. Execution
time for fft
depends on the
length, n
, of the DFT it performs;
see the fft
for details about
the algorithm.
Note
The resulting FFT amplitude is |
The inverse discrete Fourier transform function ifft
also accepts an input sequence
and, optionally, the number of desired points for the transform. Try
the example below; the original sequence x
and
the reconstructed sequence are identical (within rounding error).
t = 0:1/255:1; x = sin(2*pi*120*t); y = real(ifft(fft(x)));
This toolbox also includes functions for the two-dimensional FFT and its inverse, fft2
and ifft2
.
These functions are useful for two-dimensional signal or image processing. The goertzel
function,
which is another algorithm to compute the DFT, also is included in
the toolbox. This function is efficient for computing the DFT of a
portion of a long signal.
It is sometimes convenient to rearrange the output of the fft
or fft2
function
so the zero frequency component is at the center of the sequence.
The MATLAB function fftshift
moves
the zero frequency component to the center of a vector or
matrix.