Digital Signal Processing Reference
In-Depth Information
filter obtained from the Kaiser window is therefore the least expensive from the
implementation perspective.
For the design of optimal filters, M
ATLAB
has incorporated the
firpm
func-
tion, which has the following syntax:
fir coefficients
=
firpm(order,range norm cut off,
f response,wmatrix);
where the input argument
order
denotes the order of the FIR filter. The second
input argument
rang norm cut off
is a vector that specifies the edges of
the normalized cut-off frequency of the FIR filter. All elements of this vector
should have a value between zero and one. For a lowpass filter, the elements of
the
rang norm cut off
vector are given by
rang norm cut off
=
[0, pass band cut off, stop band cut off,
1];
The third input argument
f response
specifies the four gains of the FIR
filter at the four frequencies specified in the
rang norm cut off
vector. For
a lowpass filter, the value of the
f response
vector is given by
f response
=
[1, 1, 0, 0];
Finally, the fourth input argument
wmatrix
specifies the weight matrix. Since
wmatrix
has one entry per band, it is half the length of
rang norm cut off
and
f response
vectors.
Example 15.11 illustrates the design of an optimal FIR filter using the
firpm
function.
Example 15.11
Examples 15.9 and 15.10 designed an FIR filter using rectangular. Ham-
ming, and Kaiser windows with a given set of design specifications. It was
shown in Example 15.10 that an FIR filter of length 47, designed using a
Kaiser window, satisfies the design specifications. Design the optimal FIR filter
of length 47 using the Parks-McClellan algorithm and compare the magni-
tude frequency response with that of the FIR filter obtained using the Kaiser
window.
Solution
The values of the normalized pass- and stop-band edge frequencies are given
by
10
3
)
/
(0
.
5
2
π
8
10
3
)
normalized pass-band edge frequency
Ω
p
=
(3
π
=
0
.
375;
10
3
)
/
(0
.
5
2
π
8
10
3
)
normalized stop-band edge frequency
Ω
s
=
(4
π
=
0
.
5
.
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