An Iir-Filter Example: A Butterworth Filter

Transcription

1 An Iir-Filter Example: A Butterworth Filter Josef Goette Bern University of Applied Sciences, Biel Institute of Human Centered Engineering - microlab JosefGoette@bfhch February 7, 2017 Contents 1 Introduction 1 2 Analog Butterworth Lowpass-Filters 4 3 Continuous-to-Discrete Transformations Impulse Invariance Transformation Bilinear Transformation 23 4 Discrete-Time Butterworth Filter Example Design Using Impulse-Invariance Transformation Design Using Bilinear Transformation 39 References Iir Butterworth i 2017

2 c Josef Goette, All rights reserved This work may not be translated or copied in whole or in part without the written permission by the author, except for brief excerpts in connection with reviews or scholarly analysis Use in connection with any form of information storage and retrieval, electronic adaptation, computer software is forbidden 33 Iir Butterworth ii 2017

3 1 Introduction In the present document, we use, as usual, the following notation: Continuous-time signal frequencies come in standard characters such as f for the frequency in Hertz ([Hz]), and ω for the radian frequency in [rad/sec], ω = 2πf The corresponding discrete-time signal frequencies use the corresponding symbols with hats: ˆf ˆ= f/fs = ft s and ˆω = ωt s = ω/f s with f s being the sampling frequency in [Hz] and T s the corresponding sampling interval T s = 1/f s in [sec] We abbreviate continuous-time by Ct and discrete-time by Dt Further abbreviations are C2d for continuous-to-discrete, Tf for transfer function, and Lhp for left-half plane We very closely follow [OS75] for the developments in the present document; see our additional remarks on details, which appear after the bibliography at the very end of the document on page Iir Butterworth

4 An Iir-Filter Design Approach Iir (recursive) filters are often design by what might be called analog prototyping design a normalized Ct lowpass prototype filter then: apply frequency-band transformations such as lowpass to lowpass lowpass to highpass lowpass to bandpass lowpass to bandstop then: apply a continuous-to-discrete transformation here: we work-out a Butterworth-filter example The acronym Iir stands for infinite impulse response; theoretically, the impulse response of this kind of filters never completely dies out Of course, stable Iir filters have an impulse response that approaches more and more the zero line as time passes by; if the filter runs on a digital computer with floatingpoint arithmetic, then the impulse response will eventually become smaller than the smallest positive representable number; if it runs on a digital computer with fixed-point arithmetic, then the impulse response might begin to oscillate without end see the discussion to the keyword limit cycles in our document [Goe17] Recall that the acronym Ct means continuous time Matlab supplies the following commands for frequencyband transformations: lp2lp() for the lowpass-to-lowpass transformation; lp2hp() for the lowpass-to-highpass transformation; 33 Iir Butterworth

5 lp2bp() for the lowpass-to-bandpass transformation; and the command lp2bs() for the lowpass-to-bandstop transformation, respectively Matlab has also commands for the continuous-to-discrete transformations, some of which stem from the Control System Toolbox (use help c2d), and some others from the Signal Processing Toolbox (use help impinvar and use help bilinear) 1 As the table of contents reveals, we discuss in the present document the design based on the impulse-invariance transformation and the design based on the bilinear transformation But also note that Matlab supplies higher level commands that integrate the mentioned lower level commands and thus considerably simplify the design of discrete-time and even digital filters 2 from the perspective of the user; you might also want to try out the graphical filter design toolfdatool (use help fdatool to obtain a short description, and type fdatool in the Matlab command window to run the Gui) 1 The mentioned toolboxes just have different names for the commands; the algorithms behind these names are, however, very similar or even identical 2 Recall that we name filters to be discrete time if they filter signals that have the time being discrete but the samples being still real numbers; digital filters filter then signals where not only the time is discrete, but where also the samples are represented be a finite number of bits In the realm of Matlab simulations, we often also call filters and signals to be discrete, if the samples of the signals are represented by floating-point numbers still finitely many bits, but ; and we retain the notion digital for signals with samples that are represented by fixed-point numbers 33 Iir Butterworth

6 2 Analog Butterworth Lowpass-Filters Butterworth Filter Properties magnitude response is maximally flat in passband for a N-th order lowpass, the first (2N 1) derivatives of the squared magnitude function are zero at ω = 0 the approximation to the ideal rectangular lowpass characteristic (brick-wall) is monotonic in passband as well as in stopband squared magnitude function H Ct (jω) 2 1 = ( jω 1 + jω c specified by just 2 parameters the filter order N the 3 db cutoff-frequency ω c ) 2N We also often call these analog filters continuous-time (Ct) filters 33 Iir Butterworth

7 Magnitude-Squared and Magnitude Responses H Ct (jω) 2 1 N = 1, 2, 4, 8 1/2 0 ω c ω H Ct (jω) 1 N = 1, 2, 4, 8 1/ 2 0 ω c ω Recall that the linear gain 1/ 2 corresponds to 3 db; we have 20 log 10 (1/ 2) = Butterworth filters have a frequency magnitude response with no ripple, neither in the passband nor in the stopband As the parameter N in the squared magnitude function on page 4 increases, the filter characteristics become sharper, meaning that if N increases, the characteristic in the passband stays closer to unity whereas the characteristic in the stopband comes close to zero more rapidly Other well known analog filters are the Chebyshev filters, the inverse Chebyshev filters, and the Cauer filters The Chebyshev 33 Iir Butterworth

8 filters have a frequency magnitude response with a ripple in the passband but no ripple in the stopband; inverse Chebyshev filters have a dual characteristic to that of the Chebyshev filters: they have no ripple in the passband but a ripple in the stopband; Cauer filters also called elliptic filters have a ripple in both, in the passband as well as in the stopband Chebyshev filters are often also called Chebyshev type I filters, whereas inverse Chebyshev filters are then called Chebyshev type II filters For a comparable frequency filtering performance, Butterworth filters need the highest order, Chebyshev type I and Chebyshev type II filters need medium orders, and Cauer filters need the lowest order A higher order means a higher number of operations needed to compute an output sample, and the need of a higher number of storage cells 33 Iir Butterworth

9 Transfer Function Poles denote by H Ct (s) the transfer function of the Butterworth filter from squared magnitude function we see that (jω s) 1 H Ct (s)h Ct ( s) = ( s 1 + jω c poles of squared magnitude function are s pk = ( 1) 1/(2N) (jω c ) { ( 1 = ω c exp jπ k )} 2N ) 2N, k = 0, 1,, (2N 1) 33 Iir Butterworth

10 Transfer Function Poles: N = 3 Example for N = 3 2N = 6: s pk = ( 1) 1/6 j ω c ( = e j(π+k2π)) 1/6 e jπ/2 ω c = e jπ/6 } e jk2π/6 {{}} e jπ/2 {{} ω c spacing rotation j 1 ω c 2N = 6-th roots of (-1) corresponding Butterworth poles We see then that the poles of the Butterworth squared magnitude function have an angular spacing of 2π/(2N) = π/n for an N-th order Butterworth filter We further have for these poles that they are distributed around a circle with radius ω c ; that they are distributed symmetrically on either side of the imaginary axis; that there are no poles on the imaginary axis itself; and that there are poles on the real axis if N is odd, but not if N is even 33 Iir Butterworth

11 Transfer Function to determine the Tf H Ct (s) from the Butterworth squaredmagnitude function: perform factorization H Ct (s)h Ct ( s) observe that poles in squared magnitude function appear in pairs if s = s p is a pole then s = s p is also a pole to construct H Ct (s) from squared magnitude function choose one pole of each pair choose stable pole (in Lhp) of each pair p1 poles of N = 3-rd order Butterworth transfer-function: H Ct(s) = p 0 p 1 p 1 (s p 0 )(s p 1 )(s p 1 ) p0= ω c p 1 ω c 33 Iir Butterworth

12 3 Continuous-to-Discrete Transformations C2d-Transformation Procedures there are various procedures to transform an analog prototypefilter design into a discrete-time filter: impulse-invariance transformation procedures based on numerical solution of differential equations first forward-difference (forward Euler) first backward-difference (backward Euler) bilinear transformation We discuss in more detail the impulse-invariance transformation in Subsection 31 below For the procedures based on numerical solution of differential equations we note that the forward Euler transformation is the most simple transformation that has, however, the drawback that an unstable discrete-time system might result from a stable continuous-time (analog) prototype system The transfer function H(z) of the discrete-time filter is obtained here by replacing the variable s in the continuous-time prototype transfer 33 Iir Butterworth

13 function H Ct (s) by (z 1)/T s, where T s denotes the sampling interval, T s = 1/f s with f s being the sampling frequency: forward Euler: s = 1 ) (z 1 = 1 1 z 1 T s T s z 1 The backward Euler transformation has the advantage over the forward Euler transformation that stable continuous-time designs are transformed into stable discrete-time designs The transformation is given by backward Euler: s = 1 T s z 1 z = 1 T s (1 z 1) The bilinear transformation is the most often used transformation in the design of discrete-time filters; 3 therefore, we discuss it in more detail below in Subsection 32 For the interested reader we mention that the bilinear transformation is a member of the larger family of Moebius transformations, which are conformal mappings from complex plane to complex plane; a reference is [Hen74, Chapter 5] 3 In the design of simple discrete-time control algorithms, the Euler approximations are likewise very often used 33 Iir Butterworth

14 31 Impulse Invariance Transformation The Impulse Invariance Transformation given: continuous-time (Ct) prototype (analog prototype) H Ct (s) h Ct (t) ˆ= impulse response of Ct filter find: for the discrete-time filter H(z) h[n] ˆ= impulse response of Dt filter solution: sample the continuous-time impulse response h[n] ˆ= h Ct (t = nt s ) where T s ˆ= sampling time interval The above formulae state, reformulated in words, that the impulse response of the discrete-time filter is obtained from the impulse response of the continuous-time (analog) prototype filter through sampling of the latter 33 Iir Butterworth

15 Impulse Invariance: Transfer Functions it can be shown see the sampling process that the z-transformation of h[n] H(z) is related to the Laplace transformation of h Ct (t) H Ct (s) by H(z) z=e sts = 1 T s k= ( H Ct s + j 2π ) k T s from z = e sts : strips of width 2π/T s is s-plane map onto entire z-plane 33 Iir Butterworth

16 Impulse Invariance: Mapping of Planes 1 j z-plane = 3π T s π T s π T s 3π T s R(s) I(s) s-plane strips of width 2π/T s is s-plane map onto entire z-plane Lhp-part of each strip maps into unit circle stable poles of Ct filter go to stable poles of Dt filter imaginary axis in s-plane maps onto unit circle such that each segment of length 2π/T s is mapped once around the circle We should mention here that the mapping from the s-plane to the z-plane induced by the impulse-invariance procedure is no simple algebraic mapping, as are the mappings that result from the procedures based on numerical solutions of differential equations, an example of which the bilinear transformation we discuss in Subsection Iir Butterworth

17 Impulse Invariance: Frequency Responses the Dt frequency response is expressed in terms of the Ct prototype frequency response as note: H ( z = e jˆω) = 1 T s if and only if k= ( H Ct j ˆω + j 2π ) k T s T s H Ct (jω) 0, for ω π T s then H ( z = e jˆω) = 1 T s H Ct ( j ˆω T s ) else aliasing 33 Iir Butterworth

18 Impulse Invariance: Aliasing 1 H Ct (jω) ω c 2πf s ω 1 T s H(e jˆω ) 2π π 0 ˆω c π 2π ˆω The above figure graphically shows the effects of aliasing: In the upper panel we show the frequency response of a continuoustime filter prototype which is not bandlimited; in the lower panel we show the first three aliasing parts giving, in sum together with all other aliasing parts, the discrete-time frequency response 33 Iir Butterworth

19 Impulse Invariance: Interpretation of Mapping Ct transfer function: partial fraction decomposition H Ct (s) = N k=1 A k s s k, s k ˆ= poles h Ct (t) = N A k e skt u(t) k=1 Dt impulse response and corresponding transfer function h[n] = h Ct (nt s ) = N N A k e s knt s ( u[n] = A k e s k T s ) nu[n] k=1 k=1 H(z) = N k=1 A k 1 (e s kt s)z 1 We denote by u(t) the continuous-time unit-step function and, correspondingly, by u[n] the discrete-time unit-step function Note that the above development is true if all poles have multiplicity 1; for poles with higher multiplicities we must use the corresponding partial fraction decompositions 33 Iir Butterworth

20 Impulse Invariance: Interpretation of Mapping (2) s-plane poles s k transformation of poles: transform to z-plane poles z k = e s kt s coefficients A k are equal for Ct and Dt if Ct filter is stable, then Dt filter is also stable we note however that although poles are mapped by z k = e s kt s the complete planes are not mapped by that relation for example: zeros of Dt transfer function are functions of the coefficients A k and of the poles s k Concerning the stability of the filters we know that the continuous-time filter is stable if and only if all of its poles are in the left-half complex s-plane, real{s k } < 0 By the given mapping of the poles we then have for the discrete-time poles z k that z k = e s k T s < 1, meaning that the discrete-time poles are inside of the unit circle, and in turn, that the discrete-time filter is also stable It is important to note for the impulse-invariant transformation that, although the poles are mapped from the s-plane to the z-plane by the relation z k = e s kt s, the planes themselves are not mapped by that relation For example, zeros are not mapped in that way; the following simple example gives an illustration 33 Iir Butterworth

21 Example Consider the second-order continuous-time transfer function H Ct (s) = = s + a (s + a) 2 + b 2 1/2 s + a + jb + 1/2 s + a jb, having one zero at s = a, the second zero at infinity, and a complex-conjugate pole pair at s = a ± jb The discrete-time transfer function obtained from the impulseinvariance transformation becomes H(z) = = 1/2 1 e ats e jbts z 1 + 1/2 1 e ats e +jbts z 1 1 e ats ( e +jbt s + e jbts ) /2 z 1 (1 e ats e jbts z 1 )(1 e ats e +jbts z 1 ) 1 e ats cos(bt s )z 1 = 1 e ats (e +jbts + e jbts )z 1 + e 2aTs z 2 z ( z e ats cos(bt s ) ) = z 2 2e ats cos(bt s )z + e 2aTs This discrete-time transfer function has one zero at the origin and the other zero at z = e ats cos(bt s ) We thus see that, although the poles are mapped by z k = e s kt s, the zeros are not mapped according to this formula 33 Iir Butterworth

22 Impulse Invariance: Practical Advice if Ct prototype is sufficiently bandlimited, then H ( e jˆω) 1 T s H Ct ( j ˆω T s ) thus: high sampling rates (small T s ) will minimize the aliasing, but the Dt filter may obtain a very high gain advice: instead using use H(z) = H(z) = N k=1 N k=1 the Dt impulse response then is A k 1 (e s kt s) z 1 A k T s 1 (e s kt s) z 1 h[n] = T s h Ct (t = nt s ) 33 Iir Butterworth

23 Impulse Invariance: Generalizations one motivation to use the impulse-invariance procedure if Ct prototype filter is bandlimited then Dt filter frequency response closely approximates the Ct frequency response another motivation to use the procedure: control some aspects of the time response of Dt filter step invariance procedure waveform invariance: extend the concept to preserve the output wave-shape for a variety of inputs final remark: besides aliasing, the impulse-invariance approach transforms the frequency responses linearly The mentioned step-invariance procedure just obtains the step response of the discrete-time filter by sampling the step response of the continuous-time prototype filter The resulting discrete-time filter then might have desired step-response characteristics such as small rise time and low peak overshoot An important feature of the impulse-invariance procedure is that, besides aliasing, the frequency responses of the continuoustime prototype filter and its discrete-time counterpart are linearly related, meaning that the shape of continuous-time frequency response is preserved in the discrete-time filter This result is in contrast to the procedures which use algebraic transformations, an example of which the bilinear transformation we discuss in Subsection 32 below We recall however, that the 33 Iir Butterworth

24 impulse-invariance design technique is only appropriate for essentially bandlimited filters Thus, the design of highpass or bandstop filters requires additional bandlimiting to avoid severe aliasing distortions 33 Iir Butterworth

25 32 Bilinear Transformation Bilinear Transformation Formulae given: Tf of analog prototype H Ct (s) find: Tf H(z) of corresponding discrete-time filter solution: replace s in H Ct (s) by the inverse formula then is s = 2 T s 1 z z 1 z = 1 + (T s/2)s 1 (T s /2)s You might want to verify the given formulae as follows: Start with a first-order continuous-time prototype system and first specify it by its differential equation; second, express the differential equation by its corresponding integral equation formulated for the n-th sampling time instant and its past neighbor (n 1); third, approximate the involved integral by the trapezoidal rule; forth, replace the appearing derivatives by the expression given by the differential equation started with; fifth, rearrange terms to obtain the difference equation of the discretetime system, and from it obtain the transfer function H(z) Generalize to systems of order higher than one 33 Iir Butterworth

26 Bilinear Transformation: Frequency Mapping Ct frequencies ω on imaginary axis in s-plane: s = jω Dt frequencies ˆω on unit circle in z-plane: z = e jˆω unit circle maps to imaginary axis (and vice versa) s ( z = e jˆω) = 2 T s 1 e jˆω 1 + e jˆω = = j 2 T s tan(ˆω/2) = jω therefore, frequency mapping is ω = 2 T s tan (ˆω/2) ˆω = 2 arctan(ωt s /2) To fill-in the dots in the above derivation, first use 1 e jˆω = e j ˆω 2 next use Euler s formulae sin(ˆω/2) = ( ) ( ) e j ˆω 2 e j ˆω 2, 1+e jˆω = e j ˆω 2 e j ˆω 2 + e j ˆω 2 ; ej ˆω 2 e j ˆω 2 2j, cos(ˆω/2) = ej ˆω 2 + e j ˆω 2 to obtain j sin(ˆω/2)/ cos(ˆω/2) which is equal to j tan(ˆω/2) 2, 33 Iir Butterworth

27 Bilinear Transformation: Frequency Mapping ˆω ˆ= Dt frequency ω ˆ= Ct frequency the frequency mapping is ˆω = 2 arctan(ωt s /2) π ˆω ω π 33 Iir Butterworth

28 Bilinear Transformation: Mapping of Planes the mapping s z is z = 1 + (T s/2)s 1 (T s /2)s 1 j image of lefthalf plane image of s = jω z-plane = R(s) I(s) s-plane 33 Iir Butterworth

29 Bilinear Transformation: Mapping of Circles in s-plane: Butterworth poles on a circle with radius ω c, equally spaced in angle bilinear transformation is conformal mapping: Butterworth circle in s-plane maps to a circle in z-plane j 1 1 ω ct s/2 1+ω ct s/2 1+ω ct s/2 1 ω ct s/2 z-plane = ω c s-plane Butterworth N =3,2N =6 Note, however, that the Butterworth circle in the z-plane is neither centered at the origin, nor are the poles equally spaced in angle But the left-half s-plane poles map into poles inside of the unit circle; therefore as is always the case with the bilinear transformation we obtain a stable discrete-time Butterworth filter from a continuous-time Butterworth prototype 33 Iir Butterworth

30 Bilinear Transformation: Frequency Responses ˆω ˆω 0 3dB H(e jˆω )[db] ˆω3dB π π ˆω = 2 arctan(ωt s /2) ω H Ct (jω)[db] 0 3dB ω 3dB ω 33 Iir Butterworth

31 4 Discrete-Time Butterworth Lowpass Filter Example Frequency-Response Specifications here: specifications in the Dt frequency domain requirements: passband magnitude constant to within 1 db passband is 0 ˆω ˆω p ˆ= 02π stopband attenuation > 15 db stopband is 03π ˆ= ˆω s ˆω π thus, if passband magnitude is normalized to 1 20 log 10 H(e j02π ) 1 20 log 10 H(e j03π ) 15 for convenience we may assume that the sampling-time interval is T s ˆ= 1 Note that we must distinguish between two different application scenarios: The first scenario designs a discrete-time filter which is specified in the discrete-time frequency domain This is the situation we consider here and which allows to set the sampling time equal to one, because there exists no real continuoustime filter 33 Iir Butterworth

32 The second application scenario emulates a continuous-time filter with a discrete-time filter Here the specifications are given in the continuous-time frequency domain, and our design has to select an appropriate sampling time interval T s with which we plan to implement the emulating discrete-time filter Obviously, in this scenario the sampling time interval cannot be set to unity 33 Iir Butterworth

33 41 Design Using Impulse-Invariance Transformation Analog Prototype Specifications transform the Dt frequency specifications to corresponding specifications of a Ct prototype recall: impulse invariance design introduces aliasing beside aliasing it linearly maps Ct to Dt frequencies convenient procedure: assume that aliasing is negligible carry out design verify performance of resulting filter thus, mapping of critical frequencies is ˆω p = 02π ω p = ˆω p /T s = ˆω p 20 log 10 H Ct (jω p ) 1 ˆω s = 03π ω s = ˆω s /T s = ˆω s 20 log 10 H Ct (jω s ) 15 Recall that we have assumed that the sampling time interval T s is unity 33 Iir Butterworth

34 Analog Butterworth Parameters the Ct Butterworth squared magnitude function is H Ct (jω) 2 1 = ( ω 1 + ω c ) 2N to do: determine the needed two parameters N and ω c solving given specification inequalities with equality 4 N = 1 2 ( log log ( to meet specifications: select N = 6 ) ) = inserting N = 6 into passband equation gives ω c = Note that because we must round up N to the nearest integer, not both specifications, passband and stopband, can be met exactly If we insert N = 6 into the passband equation, the passband specifications are met exactly and the stopband specifications are exceeded for the Ct filter Such a choice allows some margin for the aliasing that enters in the Dt filter We next supply the detailed steps leading to the above results We start with the two equations in the two unknowns 4 See the derivations in the text below 33 Iir Butterworth

35 N and ω c that we obtain by setting the design specification inequalities to equality: H Ct (jω p ) 2 = H Ct (jω s ) 2 = ( ωp ω c 1 ( ωs ω c ) 2N = 10 01, (1a) 1 + ( ωp ω c ) 2N = (1b) ) 2N = 10 15, (1c) 1 + ( ωs ω c ) 2N = (1d) Taking in (1b) and (1d) logarithms (to any base) we next obtain log log ( ωp ω c ( ωs ω c ) ) = log ( ) 2N = log ( ) 2N Subtracting (2b) from (2a) we obtain = log (ω p ) log (ω c ), (2a) = log (ω s ) log (ω c ) (2b) log (ω p ) log (ω s ) = 1 (log ( ) log ( )) }{{} 2N }{{} = log (ω p /ω s ) ( ) 1 = log Iir Butterworth

36 and in turn ( ) 1 N = 1 log ( ) (3) 2 ωp log ω s Finally, we insert ω p = ˆω p = 02π and ω s = ˆω s = 03π into (3) To numerically obtain the second filter parameter, ω c = 07032, via the passband specifications, we use the parameter N = 6 and ω p = 02π in (2a) Analog Butterworth Poles for N = 6 there are 2N = 12 poles of Butterworth squaredmagnitude function these 12 poles are uniformly distributed in angle on a circle with radius ω c = the Butterworth transfer function uses the N = 6 poles in the Lhp π 6 s-plane 33 Iir Butterworth

37 Analog Butterworth Transfer Function the Lhp poles are p 1 = j p 1 = j p 2 = j p 2 = j p 3 = j p 3 = j the corresponding transfer function becomes H Ct (s) = = p 1 p 1 p 2p 2 p 3p 3 (s p 1 ) (s p 1 )(s p 2)(s p 2 )(s p 3)(s p 3 ) (s s )(s s ) 1 (s s ) We have normalized the above analog transfer function H Ct (s) such that it has a Dc gain of one 33 Iir Butterworth

38 Discrete-Time Butterworth Transfer Function express H Ct (s) as a partial fraction decomposition A k, s k apply to obtain H(z) = N k=1 A k 1 (e s kt s)z 1 ( z 1 ) H(z) = ( z z 2 ) ( ) z 1 + ( z z 2 ) ( ) z 1 + ( z z 2 ) We have obtained the above parallel form of second-order sections by combining the terms of complex-conjugate pole pairs Obviously, we might directly use this parallel form that naturally results from the impulse-invariant design procedure If we desire a cascade form or a direct form, we must combine the separate second-order terms in an appropriate way 33 Iir Butterworth

39 Discrete-Time Butterworth Magnitude-Response H(e jˆω ) ˆ= 1dB ˆ= 15dB 0 ω 02π 03π π H(e jˆω ) db ˆ= 1dB ˆ= 15dB 80 ω 02π 03π π 33 Iir Butterworth

40 Discrete-Time Butterworth Phase-Response π arg(h(e jˆω )) 0 ω 02π 03π π π Final Remarks to Our Impulse-Invariance Design We recall that we have designed the filter to exactly meet with the assumption that we have no aliasing the passband specifications, that is, in the Butterworth case, to have a 1 db attenuation at the passband edge frequency ˆω p = 02π; the design then exceeds the specification at the stopband edge frequency ˆω s = 03π As we observe from the magnitude-response plots on page 37, it is true that at the passband edge the attenuation is slightly below 15 db, indicating that the aliasing is not too strong, or, in other words, that the continuous-time filter is sufficiently bandlimited In an other design this might not be true, such that the resulting discrete-time filter does not meet the specifications If we have that situation, we may try to differently adjust the filter parameters (holding the order fixed), or we may try again with a higher-order filter 33 Iir Butterworth

41 42 Design Using Bilinear Transformation Analog Prototype Specifications Dt frequency specifications must be pre-warped to corresponding analog frequencies, such that critical analog frequencies map to correct Dt frequencies the frequency mapping function of the bilinear transformation is ω = (2/T s )tan(ˆω/2) = 2 tan(ˆω/2) thus, mapping of critical frequencies is ˆω p = 02π ω p = 2 tan(ˆω p /2) = 2 tan(01π) 20 log 10 H Ct (jω p ) 1 ˆω s = 03π ω s = 2 tan(ˆω s /2) = 2 tan(015π) 20 log 10 H Ct (jω s ) 15 Recall that we conveniently have assumed that the sampling time interval is T s = 1 33 Iir Butterworth

42 Analog Butterworth Parameters the Ct Butterworth squared magnitude function is H Ct (jω) 2 1 = ( ω 1 + ω c ) 2N to do: determine the needed two parameters N and ω c solving given specification inequalities with equality 5 N = 1 2 log ( log ) ( tan(01π) tan(015π) ) = to meet specifications: select N = 6 inserting N = 6 into stopband equation gives ω c = To supply detailed steps leading to the above results we may start with (3) on page 34 Using for the bilinear transformation ω p = 2 tan(ˆω p /2) = 2 tan(01π) and ω s = 2 tan(ˆω s /2) = 2 tan(015π), we obtain N = 1 2 ( log log ) ( ωp ω s ) = 1 2 log ( log ) ( tan(01π) tan(015π) 5 See the derivations in the accompanying text ) = Iir Butterworth

43 Because the order N of the Butterworth filter must be an integer, we must select N = 6 in order to meet the specifications If we insert N = 6 into (2b), we can determine ω c to ω c = Note that by using the equation (2b) which comes from the stopband constraint to determine the ω c parameter, we meet the stopband specifications exactly and exceed the passband specifications Such a choice is reasonable, because the bilinear transformation has no aliasing effects Analog Butterworth Poles for N = 6 there are 2N = 12 poles of Butterworth squaredmagnitude function these 12 poles are uniformly distributed in angle on a circle with radius ω c = the Butterworth transfer function uses the N = 6 poles in the Lhp π 6 s-plane 33 Iir Butterworth

44 Analog Butterworth Transfer Function the Lhp poles are p 1 = j p 1 = j p 2 = j p 2 = j p 3 = j p 3 = j the corresponding transfer function becomes H Ct (s) = = p 1 p 1 p 2p 2 p 3p 3 (s p 1 ) (s p 1 )(s p 2)(s p 2 )(s p 3)(s p 3 ) (s s )(s s ) 1 (s s ) Again, we have normalized the above analog transfer function H Ct (s) such that it has a Dc gain of one 33 Iir Butterworth

45 Discrete-Time Butterworth Transfer Function apply the bilinear transformation s = 2 T s 1 z z 1 to the analog Butterworth transfer function H Ct (s) to obtain H(z) = ( 1 + z 1) 6 ( z z 2 ) 1 ( z z 2 ) 1 ( z z 2 ) 33 Iir Butterworth

46 Discrete-Time Butterworth Magnitude-Response H(e jˆω ) ˆ= db ˆ= 15dB 0 ω 02π 03π π H(e jˆω ) db ˆ= db ˆ= 15dB 80 ω 02π 03π π 33 Iir Butterworth

47 Discrete-Time Butterworth Phase-Response π arg(h(e jˆω )) 0 ω 02π 03π π π Final Remarks to Our Bilinear-Transformation Design We recall that we have designed the filter to exactly meet the stopband specifications, that is, in the Butterworth case, to have a 15 db attenuation at the stopband-edge frequency ˆω s = 03π; the design exceeds the specification at the passband-edge frequency ˆω p = 02π As we observe from the magnitude-response plots on page 43, it is true that at the stopband edge the attenuation is at 15 db, whereas at the passband edge the attenuation is only about db, leaving a certain margin to the 1 db required by the specifications Comparison of the two Designs If we compare the bilineartransformation based design on page 43 to the design based on the impulse-invariance transformation on page 37, we see 33 Iir Butterworth

48 that the magnitude function of the bilinear transformation design falls off more rapidly than the magnitude function of the impulse-invariance transformation design This is because the bilinear transformation maps the entire jω axis of the s-plane onto the unit circle, and the continuous-time Butterworth filter of order 6 has a zero of multiplicity 6 at s ; the resulting discrete-time filter then has a zero of multiplicity 6 at z = 1 References [Goe17] Josef Goette Biomedical Signal Processing and Analysis On Fixed-Point Filter Realizations Bern University of Applied Sciences, Script at the Bfh-ti Biel/Bienne, HuCE-microLab, February 2017 [Hen74] Peter Henrici Applied and Computational Complex Analysis, volume 1 of Pure and Applied Mathematics John Wiley & Sons, New York, 1974 [OS75] Alan W Oppenheim and Ronald W Schafer Digital Signal Processing Prentice-Hall Inc, Englewood Cliffs, NJ, 1975 [Ran02] Rangaraj M Rangayyan Biomedical Signal Analysis: A Case-Study Approach IEEE Press, New York, 2002 Bfh-ti Biel/Bienne Library 5708 RANGA In our development, we have mainly followed [OS75, Chapter 5, Sections 51 and 52]; there you also find design examples for Chebyshev and Cauer lowpass filters, as well as examples for using frequency transformations to design filters with highpass, bandpass, and bandstop characteristics On Butterworth filters you might also want to consult [Ran02, Chapter 3, pp 118 ff] 33 Iir Butterworth