While other types of filters are often of interest, this note focuses on the
lowpass linear phase filter. Even though it is not immediately obvious, virtually
all of the analytical results developed in this note apply to the other types
as well. This fact is amplified in the module Extension to Non-lowpass Filters.
It is known that the Parks-McClellan filter synthesis software package produces
“optimal" filters in the sense that the best possible filter performance is
attained for the number of “filter taps" allowed by the designer. “Optimal"
can be defined various ways. The Parks-McClellan package uses the Remez
exchange algorithm to optimize the filter design by selecting the impulse
response of given length, termed here NN, which minimizes the peak ripple in
the passband and stopband. It can be shown, though not here, that minimizing
the peak, or maximum, ripple is equivalent to making all of the local peaks
in the ripple equal to each other. This fact leads to three different names for
essentially the same filter design. They are commonly called “equal-ripple"
filters, because the local peaks are equal in deviation from the desired filter
response. Because the maximum ripple deviation is minimized in this optimization
procedure, they are also termed “minimax" filters. Finally, since the Russian
Chebyshev is usually associated with minimax designs
,
these filters are often given his name.
The design template for an equal-ripple lowpass filter is shown in
Figure 1.
The passband extends from 0 Hz to the cutoff frequency denoted fcfc.
The gain in the passband is assumed to be unity. Any other gain is
attained by scaling the whole impulse response appropriately. The stopband
begins at the frequency denoted fstfst and ends at the so-called Nyquist
or “folding" frequency, denoted by fs2fs2, where fsfs is the
sampling frequency of the data entering the digital filter. In some
references, [1] for example, the sampling rate fsfs is assumed to be
normalized to unity just as the passband gain has here. The dependence on
the sampling frequency is kept explicit in this note, however, so that its
impact on design parameters can be kept visible.
The optimal synthesis algorithm is assumed here to produce an impulse response
whose associated frequency response has ripples in both the passband
and the stopband. The peak deviation in the passband is denoted δ1δ1
and the peak deviation in the stopband is denoted δ2δ2. It is commonly
thought that an “equal-ripple" design forces δ1δ1 to equal δ2δ2.
In fact this is not true. The local ripple peaks in the passband will all
equal δ1δ1 and those in the stopband will all equal δ2δ2. For
a given filter specification the two are linked together by a weight denoted
WW, so that δ1=Wδ2δ1=Wδ2. In fact the Parks-McClellan routines
insure the design of weighted equal-ripple filters. The choice of WW is
discussed shortly.
An important design parameter is the transition band, denoted ΔfΔf,
and defined as the difference between the stopband edge fstfst and the
passband edge fcfc. Thus,
Δ
f
=
f
s
t
-
f
c
.
Δ
f
=
f
s
t
-
f
c
.
(1)
In theory the required filter order NN is a function of all of the
design parameters defined so far, that is, fs,fc,fst,δ1fs,fc,fst,δ1, and δ2δ2. The central point of this technical note is
that under a large range of practical circumstances the required value of
NN can be estimated using only fs,Δffs,Δf, and the smaller of
δ1δ1 and δ2δ2.
While the parameters defined in the previous section relate directly
to the theory of FIR filter design optimization, some of them differ
from those usually employed to specify the performance of a filter. We
discuss here the conversion of two of those, δ1δ1 and δ2δ2,
into more traditional measures.
Passband Ripple:
Figure 1 uses the
parameter δ1δ1 to describe the peak difference between the template
lowpass filter and the magnitude of the filter response actually attained.
Traditionally this passband ripple has been specified in terms of the
maximum difference in the power level transmitted through the filter in
the passband. By this definition, the peak-to-peak
passband ripple, abbreviated here as
PBR, is given by
P
B
R
=
10
l
o
g
10
(
1
+
δ
1
)
2
(
1
-
δ
1
)
2
.
P
B
R
=
10
l
o
g
10
(
1
+
δ
1
)
2
(
1
-
δ
1
)
2
.
(2)
Assuming that the nominal power transmission through the filter is unity,
the numerator is the power gain at a ripple peak and the denominator is
the gain at a trough. It is easily shown (see Appendix A)
that when δ1δ1 is small compared to unity, or, equivalently, when the
passband ripple is less than about 1.5 dB, then
P
B
R
≈
17
.
36
δ
1
.
P
B
R
≈
17
.
36
δ
1
.
(3)
Stopband Ripple:
The traditional specification for
stopband ripple, abbreviated here as SBR, is the power difference between
the nominal passband transmission level and the transmission level of the
highest ripple in the stopband. For the equal ripple design shown in
Figure 1, all stopband ripples have equal peak values and
the nominal passband transmission is unity, that is, 0 dB. The stopband
ripple, or more accurately, the minimum stopband power rejection, denoted
SBR, is given by
S
B
R
=
20
l
o
g
10
δ
2
.
S
B
R
=
20
l
o
g
10
δ
2
.
(4)
Suppose a filter is specified to have a peak-to-peak
passband ripple of 0.5 dB and a minimum stopband attenuation of 60 dB.
Using the above equations we find that δ1=0.0288δ1=0.0288,
δ2=.001δ2=.001, and the relative weighting, WW, therefore equals 28.8.
♠♠
In discussing filter specifications it should be noted that the cutoff
frequency fcfc shown in Figure 1 differs from the
definition typically used in analog filter designs. The cutoff
frequency is commonly defined as the
3 dB point, that is,
that frequency at which the power transfer function falls
to a value 3 dB below the nominal passband level. Instead the value of fcfc
shown in Figure 1 is the highest frequency at which the
specified passband ripple is still attained. In very few practical cases do the
two definitions result in the same value.