The subject of digital filter design is enormous--much larger than we can hope to address in this book. However, a surprisingly large number of applications can be addressed using small filter sections which are easily designed by hand, as exemplified in Appendix B. This appendix describes some of the ``classic'' methods for IIR filter design based on the bilinear transformation of prototype analog filters, followed by the simple but powerful weighted equation error method for general purpose IIR design. For further information on digital filter design, see the documentation for the Matlab Toolboxes for Signal Processing and Filter Design, and/or [64,68,60,78].
Lowpass Filter Design
We have discussed in detail (Chapter 1) the simplest lowpass filter, having the transfer function with one zero at and one pole at . From the graphical method for visualizing the amplitude response (§8.2), we see that this filter totally rejects signal energy at half the sampling rate, while lower frequencies experience higher gains, reaching a maximum at . We also see that the pole at has no effect on the amplitude response.
A high quality lowpass filter should look more like the ``box car'' amplitude response shown in Fig.1.1. While it is impossible to achieve this ideal response exactly using a finite-order filter, we can come arbitrarily close. We can expect the amplitude response to improve if we add another pole or zero to the implementation.
Perhaps the best known ``classical'' methods for lowpass filter designs are those derived from analog Butterworth, Chebyshev, and Elliptic Function filters . These generally yield IIR filters with the same number of poles as zeros. When an FIR lowpass filter is desired, different design methods are used, such as the window method [68, p. 88] (Matlab functions fir1 and fir2), Remez exchange algorithm [68, pp. 136-140], [64, pp. 89-106] (Matlab functions remez and cremez), linear programming , [68, p. 140], and convex optimization . This section will describe only Butterworth IIR lowpass design in some detail. For the remaining classical cases (Chebyshev, Inverse Chebyshev, and Elliptic), see, e.g., [64, Chapter 7] and/or Matlab/Octave functions butter, cheby1, cheby2, and ellip.
Almost all methods for filter design are optimal in some sense, and the choice of optimality determines nature of the design. Butterworth filters are optimal in the sense of having a maximally flat amplitude response, as measured using a Taylor series expansion about dc [64, p. 162]. Of course, the trivial filter has a perfectly flat amplitude response, but that's an allpass, not a lowpass filter. Therefore, to constrain the optimization to the space of lowpass filters, we need constraints on the design, such as and . That is, we may require the dc gain to be 1, and the gain at half the sampling rate to be 0.
It turns out Butterworth filters (as well as Chebyshev and Elliptic Function filter types) are much easier to design as analog filters which are then converted to digital filters. This means carrying out the design over the plane instead of the plane, where the plane is the complex plane over which analog filter transfer functions are defined. The analog transfer function is very much like the digital transfer function , except that it is interpreted relative to the analog frequency axis (the `` axis'') instead of the digital frequency axis (the ``unit circle''). In particular, analog filter poles are stable if and only if they are all in the left-half of the plane, i.e., their real parts are negative. An introduction to Laplace transforms is given in Appendix D, and an introduction to converting analog transfer functions to digital transfer functions using the bilinear transform appears in §I.3.
where is the desired order (number of poles). This simple result is obtained when the response is taken to be maximally flat at as well as dc (i.e., when both and are maximally flat at dc).I.1Also, an arbitrary scale factor for has been set such that the cut-off frequency (-3dB frequency) is rad/sec.
The analytic continuation (§D.2) of to the whole -plane may be obtained by substituting to obtain
In the second-order case, we have, for the analog prototype,
To convert this to digital form, we apply the bilinear transform
Note that the numerator is , as predicted earlier. As a check, we can verify that the dc gain is 1:
In the analog prototype, the cut-off frequency is rad/sec, where, from Eq.(I.1), the amplitude response is . Since we mapped the cut-off frequency precisely under the bilinear transform, we expect the digital filter to have precisely this gain. The digital frequency response at one-fourth the sampling rate is
and dB as expected.
Note from Eq.(I.8) that the phase at cut-off is exactly -90 degrees in the digital filter. This can be verified against the pole-zero diagram in the plane, which has two zeros at , each contributing +45 degrees, and two poles at , each contributing -90 degrees. Thus, the calculated phase-response at the cut-off frequency agrees with what we expect from the digital pole-zero diagram.
In the plane, it is not as easy to use the pole-zero diagram to calculate the phase at , but using Eq.(I.3), we quickly obtain
A related example appears in §9.2.4.
Digitizing Analog Filters with the
The desirable properties of many filter types (such as lowpass, highpass, and bandpass) are preserved very well by the mapping called the bilinear transform.
The bilinear transform may be defined by
where is an arbitrary positive constant that we may set to map one analog frequency precisely to one digital frequency. In the case of a lowpass or highpass filter, is typically used to set the cut-off frequency to be identical in the analog and digital cases.
It is easy to check that the bilinear transform gives a one-to-one, order-preserving, conformal map  between the analog frequency axis and the digital frequency axis , where is the sampling interval. Therefore, the amplitude response takes on exactly the same values over both axes, with the only defect being a frequency warping such that equal increments along the unit circle in the plane correspond to larger and larger bandwidths along the axis in the plane . Some kind of frequency warping is obviously unavoidable in any one-to-one map because the analog frequency axis is infinite while the digital frequency axis is finite. The relation between the analog and digital frequency axes may be derived immediately from Eq.(I.9) as
Given an analog cut-off frequency , to obtain the same cut-off frequency in the digital filter, we set
Analog Prototype Filter
Since the digital cut-off frequency may be set to any value, irrespective of the analog cut-off frequency, it is convenient to set the analog cut-off frequency to . In this case, the bilinear-transform constant is simply set to
Examples of using the bilinear transform to ``digitize'' analog filters may be found in §I.2 and [64,5,6,103,86]. Bilinear transform design is also inherent in the construction of wave digital filters [25,86].
Filter Design by Minimizing the
L2 Equation-Error Norm
One of the simplest formulations of recursive digital filter design is based on minimizing the equation error. This method allows matching of both spectral phase and magnitude. Equation-error methods can be classified as variations of Prony's method . Equation error minimization is used very often in the field of system identification [46,30,78].
with given, such that some norm of the error
The approximate filter is typically constrained to be stable, and since positive powers of do not appear in , stability implies causality. Consequently, the impulse response of the filter is zero for . If were noncausal, all impulse-response components for would be approximated by zero.
Equation Error Formulation
The equation error is defined (in the frequency domain) as
while the output error is the difference between the ideal and approximate filter outputs:
Denote the norm of the equation error by
where is the vector of unknown filter coefficients. Then the problem is to minimize this norm with respect to . What makes the equation-error so easy to minimize is that it is linear in the parameters. In the time-domain form, it is clear that the equation error is linear in the unknowns . When the error is linear in the parameters, the sum of squared errors is a quadratic form which can be minimized using one iteration of Newton's method. In other words, minimizing the norm of any error which is linear in the parameters results in a set of linear equations to solve. In the case of the equation-error minimization at hand, we will obtain linear equations in as many unknowns.
Note that (I.11) can be expressed as
Audio filter designs typically benefit from an error weighting function that weights frequencies according to their audibility. An oversimplified but useful weighting function is simply , in which low frequencies are deemed generally more important than high frequencies. Audio filter designs also typically improve when using a frequency warping, such as described in [88,78] (and similar to that in §I.3.2). In principle, the effect of a frequency-warping can be achieved using a weighting function, but in practice, the numerical performance of a frequency warping is often much better.
A problem with equation-error methods is that stability of the filter design is not guaranteed. When an unstable design is encountered, one common remedy is to reflect unstable poles inside the unit circle, leaving the magnitude response unchanged while modifying the phase of the approximation in an ad hoc manner. This requires polynomial factorization of to find the filter poles, which is typically more work than the filter design itself.
A better way to address the instability problem is to repeat the filter design employing a bulk delay. This amounts to replacing by
Unstable equation-error designs are especially likely when is noncausal. Since there are no constraints on where the poles of can be, one can expect unstable designs for desired frequency-response functions having a linear phase trend with positive slope.
In the other direction, experience has shown that best results are obtained when is minimum phase, i.e., when all the zeros of are inside the unit circle. For a given magnitude, , minimum phase gives the maximum concentration of impulse-response energy near the time origin . Consequently, the impulse-response tends to start large and decay immediately. For non-minimum phase , the impulse-response may be small for the first samples, and the equation error method can yield very poor filters in these cases. To see why this is so, consider a desired impulse-response which is zero for , and arbitrary thereafter. Transforming into the time domain yields
where ``'' denotes convolution, and the additive decomposition is due the fact that for . In this case the minimum occurs for ! Clearly this is not a particularly good fit. Thus, the introduction of bulk-delay to guard against unstable designs is limited by this phenomenon.
It should be emphasized that for minimum-phase , equation-error methods are very effective. It is simple to convert a desired magnitude response into a minimum-phase frequency-response by use of cepstral techniques [22,60] (see also the appendix below), and this is highly recommended when minimizing equation error. Finally, the error weighting by can usually be removed by a few iterations of the Steiglitz-McBride algorithm.
An FFT-Based Equation-Error Method
The algorithm below minimizes the equation error in the frequency-domain. As a result, it can make use of the FFT for speed. This algorithm is implemented in Matlab's invfreqz() function when no iteration-count is specified. (The iteration count gives that many iterations of the Steiglitz-McBride algorithm, thus transforming equation error to output error after a few iterations. There is also a time-domain implementation of the Steiglitz-McBride algorithm called stmcb() in the Matlab Signal Processing Toolbox, which takes the desired impulse response as input.)
Since is a quadratic form, the solution is readily obtained by equating the gradient to zero. An easier derivation follows from minimizing equation error variance in the time domain and making use of the orthogonality principle . This may be viewed as a system identification problem where the known input signal is an impulse, and the known output is the desired impulse response. A formulation employing an arbitrary known input is valuable for introducing complex weighting across the frequency grid, and this general form is presented. A detailed derivation appears in [78, Chapter 2], and here only the final algorithm is given:
Given spectral output samples and input samples , we minimize
Let : denote the column vector determined by , for filled in from top to bottom, and let : denote the size symmetric Toeplitz matrix consisting of : in its first column. A nonsymmetric Toeplitz matrix may be specified by its first column and row, and we use the notation :: to denote the by Toeplitz matrix with left-most column : and top row :. The inverse Fourier transform of is defined as
where the overbar denotes complex conjugation, and four corresponding Toeplitz matrices,
where negative indices are to be interpreted mod , e.g., .
The solution is then
There are several variations on equation-error minimization, and some confusion in terminology exists. We use the definition of Prony's method given by Markel and Gray . It is equivalent to ``Shank's method'' . In this method, one first computes the denominator by minimizing
This step is equivalent to minimization of ratio error (as used in linear prediction) for the all-pole part , with the first terms of the time-domain error sum discarded (to get past the influence of the zeros on the impulse response). When , it coincides with the covariance method of linear prediction [48,47]. This idea for finding the poles by ``skipping'' the influence of the zeros on the impulse-response shows up in the stochastic case under the name of modified Yule-Walker equations .
Now, Prony's method consists of next minimizing output error with the pre-assigned poles given by . In other words, the numerator is found by minimizing
The Padé-Prony Method
Another variation of Prony's method, described by Burrus and Parks  consists of using Padé approximation to find the numerator after the denominator has been found as before. Thus, is found by matching the first samples of , viz., . This method is faster, but does not generally give as good results as the previous version. In particular, the degenerate example gives here as did pure equation error. This method has been applied also in the stochastic case .
On the whole, when is causal and minimum phase (the ideal situation for just about any stable filter-design method), the variants on equation-error minimization described in this section perform very similarly. They are all quite fast, relative to algorithms which iteratively minimize output error, and the equation-error method based on the FFT above is generally fastest.
A View of Linear Time Varying Digital Filters