Digital Signal Processing Reference
In-Depth Information
thoroughly analyzed in the next section. We can already say, however, that
while the minimum square error is an integral criterion, the minimax is a
pointwise criterion; or, mathematically, that the MSE and the minimax are
respectively L 2 [ π , π ] -and L [ π , π ] -norm minimizations for the er-
ror function E
l g r , y i d . , © , L s
( ω )= H
e j ω )
e j ω )
. Figure 7.11 illustrates the typical result
of applying both criteria to the ideal lowpass problem. As can be seen, the
minimum square and minimax solutions are very different.
(
H
(
1
1
0
0
0.4
π
0.4
π
0
π/
2
0
π/
2
Figure 7.11 Error shapes in passband for MSE and minimax optimization meth-
ods.
7.2.2 Minimax FIR Filter Design
As we saw in the opening example, FIR filter design by windowing mini-
mizes the overall mean square error between the desired frequency response
and the actual response of the filter. Since this might lead to a very large er-
ror at frequencies near the transition band, we now consider a different ap-
proach, namely the design by minimax optimization. This technique mini-
mizes the maximum allowable error in the filter's magnitude response, both
in the passband and in the stopband. Optimality in the minimax sense re-
quires therefore the explicit stating of a set of tolerances in the prototypi-
cal frequency response, in the form of design specifications as seen in Sec-
tion 7.1.2. Before tackling the design procedure itself, we will need a series
of intermediate results.
Generalized Linear Phase. In Section 5.4.3, we introduced the concept
of linear phase; a filter with linear phase response is particularly desirable
since the phase response translates to just a time delay (possibly fractional)
and we can concentrate on the magnitude response only. We also intro-
duced the notion of group delay and showed that linear phase corresponds
to constant group delay. Clearly, the converse is not true: a frequency re-
sponse of the type
e j ω )= H
e j ω ) e −j ω d + j α
H
(
(
 
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