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Feb.2008DISP Lab1 FIR and IIR Filter Design Techniques FIR IIR Speaker: Wen-Fu Wang Advisor: Jian-Jiun Ding Graduate Institute.

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Presentation on theme: "Feb.2008DISP Lab1 FIR and IIR Filter Design Techniques FIR IIR Speaker: Wen-Fu Wang Advisor: Jian-Jiun Ding Graduate Institute."— Presentation transcript:

1 Feb.2008DISP Lab1 FIR and IIR Filter Design Techniques FIR IIR Speaker: Wen-Fu Wang Advisor: Jian-Jiun Ding Graduate Institute of Communication Engineering National Taiwan University, Taipei, Taiwan, ROC

2 Feb.2008DISP Lab2 Outline Introduction IIR Filter Design by Impulse invariance method IIR Filter Design by Bilinear transformation method FIR Filter Design by Window function technique

3 Feb.2008DISP Lab3 Outline FIR Filter Design by Frequency sampling technique FIR Filter Design by MSE Conclusions References

4 Feb.2008DISP Lab4 Introduction Basic filter classification We put emphasis on the digital filter now, and will introduce to the design method of the FIR filter and IIR filter respectively. Filter Analog Filter Digital Filter IIR Filter FIR Filter

5 Feb.2008DISP Lab5 Introduction IIR is the infinite impulse response abbreviation. Digital filters by the accumulator, the multiplier, and it constitutes IIR filter the way, generally may divide into three kinds, respectively is Direct form, Cascade form, and Parallel form.

6 Feb.2008DISP Lab6 Introduction IIR filter design methods include the impulse invariance, bilinear transformation, and step invariance. We must emphasize at impulse invariance and bilinear transformation.

7 Feb.2008DISP Lab7 Introduction IIR filter design methods Continuous frequency band transformation Impulse Invariance method Bilinear transformation method Step invariance method IIR filter Normalized analog lowpass filter

8 Feb.2008DISP Lab8 Introduction The structures of IIR filter Direct form 1 Direct form2 b0 b1 b2 b1 b0 -a1 -a2 -a1 -a2 x(n) Y(n)

9 Feb.2008DISP Lab9 Introduction The structures of IIR filter Cascade form x(n)Y(n) b0 b1 b2 -a1 -a2 -c1 -c2 d1 d2 Parallel form Y(n)x(n) b1 b0 d1 d0 E -c1 -c2 -a1 -a2

10 Feb.2008DISP Lab10 Introduction FIR is the finite impulse response abbreviation, because its design construction has not returned to the part which gives. Its construction generally uses Direct form and Cascade form.

11 Feb.2008DISP Lab11 Introduction FIR filter design methods include the window function, frequency sampling, minimize the maximal error, and MSE. We must emphasize at window function, frequency sampling, and MSE. Window function technique Frequency sampling technique Minimize the maximal error FIR filter Mean square error

12 Feb.2008DISP Lab12 Introduction The structures of FIR filter x(n) b1 b2 b3 b4 b0 Y(n) Direct formCascade form b1 b2 d1 d2 b0

13 Feb.2008DISP Lab13 IIR Filter Design by Impulse invariance method The most straightforward of these is the impulse invariance transformation Let be the impulse response corresponding to, and define the continuous to discrete time transformation by setting We sample the continuous time impulse response to produce the discrete time filter

14 Feb.2008DISP Lab14 IIR Filter Design by Impulse invariance method The frequency response is the Fourier transform of the continuous time function and hence

15 Feb.2008DISP Lab15 IIR Filter Design by Impulse invariance method The system function is It is the many-to-one transformation from the s plane to the z plane.

16 Feb.2008DISP Lab16 IIR Filter Design by Impulse invariance method The impulse invariance transformation does map the -axis and the left-half s plane into the unit circle and its interior, respectively Re(Z) Im(Z) 1 S domainZ domain

17 Feb.2008DISP Lab17 IIR Filter Design by Impulse invariance method is an aliased version of The stop-band characteristics are maintained adequately in the discrete time frequency response only if the aliased tails of are sufficiently small. 0

18 Feb.2008DISP Lab18 IIR Filter Design by Impulse invariance method The Butterworth and Chebyshev-I lowpass designs are more appropriate for impulse invariant transformation than are the Chebyshev-II and elliptic designs. This transformation cannot be applied directly to highpass and bandstop designs.

19 Feb.2008DISP Lab19 IIR Filter Design by Impulse invariance method is expanded a partial fraction expansion to produce We have assumed that there are no multiple poles And thus

20 Feb.2008DISP Lab20 IIR Filter Design by Impulse invariance method Example: Expanding in a partial fraction expansion, it produce The impulse invariant transformation yields a discrete time design with the system function

21 Feb.2008DISP Lab21 IIR Filter Design by Bilinear transformation method The most generally useful is the bilinear transformation. To avoid aliasing of the frequency response as encountered with the impulse invariance transformation. We need a one-to-one mapping from the s plane to the z plane. The problem with the transformation is many-to-one.

22 Feb.2008DISP Lab22 IIR Filter Design by Bilinear transformation method We could first use a one-to-one transformation from to, which compresses the entire s plane into the strip Then could be transformed to z by with no effect from aliasing. s domain

23 Feb.2008DISP Lab23 IIR Filter Design by Bilinear transformation method The transformation from to is given by The characteristic of this transformation is seen most readily from its effect on the axis. Substituting and, we obtain

24 Feb.2008DISP Lab24 IIR Filter Design by Bilinear transformation method The axis is compressed into the interval for in a one-to- one method The relationship between and is nonlinear, but it is approximately linear at small. -

25 Feb.2008DISP Lab25 IIR Filter Design by Bilinear transformation method The desired transformation to is now obtained by inverting to produce And setting, which yields Re(Z) Im(Z) 1 S domainZ domain

26 Feb.2008DISP Lab26 IIR Filter Design by Bilinear transformation method The discrete-time filter design is obtained from the continuous-time design by means of the bilinear transformation Unlike the impulse invariant transformation, the bilinear transformation is one-to-one, and invertible.

27 Feb.2008DISP Lab27 FIR Filter Design by Window function technique Simplest FIR the filter design is window function technique A supposition ideal frequency response may express where

28 Feb.2008DISP Lab28 FIR Filter Design by Window function technique To get this kind of systematic causal FIR to be approximate, the most direct method intercepts its ideal impulse response!

29 Feb.2008DISP Lab29 FIR Filter Design by Window function technique Truncation of the Fourier series produces the familiar Gibbs phenomenon It will be manifested in, especially if is discontinuous.

30 Feb.2008DISP Lab30 FIR Filter Design by Window function technique 1.Rectangular window 2.Triangular window (Bartett window)

31 Feb.2008DISP Lab31 FIR Filter Design by Window function technique 1.Rectangular window 2.Triangular window (Bartett window)

32 Feb.2008DISP Lab32 FIR Filter Design by Window function technique 3.HANN window 4.Hamming window

33 Feb.2008DISP Lab33 FIR Filter Design by Window function technique 3.HANN window 4.Hamming window

34 Feb.2008DISP Lab34 FIR Filter Design by Window function technique 5.Kaisers window 6.Blackman window

35 Feb.2008DISP Lab35 5.Kaisers window 6.Blackman window FIR Filter Design by Window function technique

36 Feb.2008DISP Lab36 FIR Filter Design by Window function technique WindowPeak sidelobe level (dB) Transition bandwidth Max. stopband ripple(dB) Rectangular Hann Hamming Blackman

37 Feb.2008DISP Lab37 FIR Filter Design by Frequency sampling technique For arbitrary, non-classical specifications of, the calculation of,n=0,1,…,M, via an appropriate approximation can be a substantial computation task. It may be preferable to employ a design technique that utilizes specified values of directly, without the necessity of determining

38 Feb.2008DISP Lab38 FIR Filter Design by Frequency sampling technique We wish to derive a linear phase IIR filter with real nonzero. The impulse response must be symmetric where are real and denotes the integer part

39 Feb.2008DISP Lab39 FIR Filter Design by Frequency sampling technique It can be rewritten as where and Therefore, it may write where

40 Feb.2008DISP Lab40 FIR Filter Design by Frequency sampling technique with corresponding transform where Hence which has a linear phase

41 Feb.2008DISP Lab41 FIR Filter Design by Frequency sampling technique The magnitude response which has a maximum value at where

42 Feb.2008DISP Lab42 FIR Filter Design by Frequency sampling technique The only nonzero contribution to at is from, and hence that Therefore, by specifying the DFT samples of the desired magnitude response at the frequencies, and setting

43 Feb.2008DISP Lab43 FIR Filter Design by Frequency sampling technique We produce a filter design from equation (5.1) for which The desired and actual magnitude responses are equal at the N frequencies

44 Feb.2008DISP Lab44 FIR Filter Design by Frequency sampling technique In between these frequencies, is interpolated as the sum of the responses, and its magnitude does not, equal that of

45 Feb.2008DISP Lab45 FIR Filter Design by Frequency sampling technique Example: For an ideal lowpass filter from, we would choose The frequency samples are indeed equal to the desired

46 Feb.2008DISP Lab46 FIR Filter Design by Frequency sampling technique The response is very similar to the result form using the rectangular window, and the stopband is similarly disappointing. We can try to search for the optimum value of the transition sample would quickly lead us to a value of approximately,

47 Feb.2008DISP Lab47 FIR Filter Design by MSE : The spectrum of the filter we obtain : The spectrum of the desired filter MSE=

48 Feb.2008DISP Lab48 FIR Filter Design by MSE Larger MSE, but smaller maximal error Smaller MSE, but larger maximal error

49 Feb.2008DISP Lab49 FIR Filter Design by MSE 1.

50 Feb.2008DISP Lab50 FIR Filter Design by MSE 2. when n, when n =, n 0, when n =, n = 0, 3. The formula can be repressed as:

51 Feb.2008DISP Lab51 FIR Filter Design by MSE 4. Doing the partial differentiation: 5. Minimize MSE: for all ns

52 Feb.2008DISP Lab52 Conclusions FIR advantage: 1. Finite impulse response 2. It is easy to optimalize 3. Linear phase 4. Stable FIR disadvantage: 1. It is hard to implementation than IIR

53 Feb.2008DISP Lab53 Conclusions IIR advantage: 1. It is easy to design 2. It is easy to implementation IIR disadvantage: 1. Infinite impulse response 2. It is hard to optimalize than FIR 3. Non-stable

54 Feb.2008DISP Lab54 References [1]B. Jackson, Digital Filters and Signal Processing, Kluwer Academic Publishers 1986 [2] Dr. DePiero, Filter Design by Frequency Sampling, CalPoly State University [3] W.James MacLean, FIR Filter Design Using Frequency Sampling [4],, 2005 [5] Maurice G.Bellanger, Adaptive Digital Filters second edition, Marcel dekker 2001

55 Feb.2008DISP Lab55 References [6] Lawrence R. Rabiner, Linear Program Design of Finite Impulse Response Digital Filters, IEEE 1972 [7] Terrence J mc Creary, On Frequency Sampling Digital Filters, IEEE 1972


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