Topic 7: Filter types and structures

Similar documents
Topic 4: The Z Transform

ECE503: Digital Signal Processing Lecture 6

z-transform Chapter 6

ECE503: Digital Signal Processing Lecture 5

LECTURE NOTES DIGITAL SIGNAL PROCESSING III B.TECH II SEMESTER (JNTUK R 13)

Lecture 2 OKAN UNIVERSITY FACULTY OF ENGINEERING AND ARCHITECTURE

Lecture 7 - IIR Filters

Digital Filter Structures

ELEN E4810: Digital Signal Processing Topic 4: The Z Transform. 1. The Z Transform. 2. Inverse Z Transform

( ) John A. Quinn Lecture. ESE 531: Digital Signal Processing. Lecture Outline. Frequency Response of LTI System. Example: Zero on Real Axis

Subband Coding and Wavelets. National Chiao Tung University Chun-Jen Tsai 12/04/2014

Lecture 19 IIR Filters

Z-Transform. 清大電機系林嘉文 Original PowerPoint slides prepared by S. K. Mitra 4-1-1

2. Typical Discrete-Time Systems All-Pass Systems (5.5) 2.2. Minimum-Phase Systems (5.6) 2.3. Generalized Linear-Phase Systems (5.

Basic Multi-rate Operations: Decimation and Interpolation

# FIR. [ ] = b k. # [ ]x[ n " k] [ ] = h k. x[ n] = Ae j" e j# ˆ n Complex exponential input. [ ]Ae j" e j ˆ. ˆ )Ae j# e j ˆ. y n. y n.

Discrete Time Systems

UNIVERSITY OF OSLO. Please make sure that your copy of the problem set is complete before you attempt to answer anything.

Digital Filter Structures. Basic IIR Digital Filter Structures. of an LTI digital filter is given by the convolution sum or, by the linear constant

EE 225a Digital Signal Processing Supplementary Material

ECE4270 Fundamentals of DSP Lecture 20. Fixed-Point Arithmetic in FIR and IIR Filters (part I) Overview of Lecture. Overflow. FIR Digital Filter

EE 521: Instrumentation and Measurements

Use: Analysis of systems, simple convolution, shorthand for e jw, stability. Motivation easier to write. Or X(z) = Z {x(n)}

Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science. Fall Solutions for Problem Set 2

Detailed Solutions to Exercises

ELEN E4810: Digital Signal Processing Topic 11: Continuous Signals. 1. Sampling and Reconstruction 2. Quantization

Digital Signal Processing

Digital Signal Processing. Midterm 1 Solution

MULTIRATE SYSTEMS. work load, lower filter order, lower coefficient sensitivity and noise,

Discrete Time Systems

Multirate Digital Signal Processing

Solutions: Homework Set # 5

Filter Banks II. Prof. Dr.-Ing. G. Schuller. Fraunhofer IDMT & Ilmenau University of Technology Ilmenau, Germany

UNIVERSITY OF OSLO. Faculty of mathematics and natural sciences. Forslag til fasit, versjon-01: Problem 1 Signals and systems.

Voiced Speech. Unvoiced Speech

Analog LTI system Digital LTI system

ELEG 305: Digital Signal Processing

Poles and Zeros in z-plane

EECE 301 Signals & Systems Prof. Mark Fowler

Implementation of Discrete-Time Systems

Discrete-time signals and systems

Lecture 11 FIR Filters

Multirate signal processing

VALLIAMMAI ENGINEERING COLLEGE. SRM Nagar, Kattankulathur DEPARTMENT OF INFORMATION TECHNOLOGY. Academic Year

Ch. 7: Z-transform Reading

Digital Signal Processing Lecture 8 - Filter Design - IIR

! Introduction. ! Discrete Time Signals & Systems. ! Z-Transform. ! Inverse Z-Transform. ! Sampling of Continuous Time Signals

REAL TIME DIGITAL SIGNAL PROCESSING

/ (2π) X(e jω ) dω. 4. An 8 point sequence is given by x(n) = {2,2,2,2,1,1,1,1}. Compute 8 point DFT of x(n) by

Chap 2. Discrete-Time Signals and Systems

Digital Signal Processing Lecture 10 - Discrete Fourier Transform

ELEG 305: Digital Signal Processing

Review of Fundamentals of Digital Signal Processing

Digital Filter Structures

Lecture 7 Discrete Systems

Signals and Systems. Problem Set: The z-transform and DT Fourier Transform

z-transforms Definition of the z-transform Chapter

Very useful for designing and analyzing signal processing systems

Lecture 14: Minimum Phase Systems and Linear Phase

Probability Space. J. McNames Portland State University ECE 538/638 Stochastic Signals Ver

Digital Signal Processing Lecture 9 - Design of Digital Filters - FIR

Topics: Discrete transforms; 1 and 2D Filters, sampling, and scanning

Oversampling Converters

How to manipulate Frequencies in Discrete-time Domain? Two Main Approaches

Review of Fundamentals of Digital Signal Processing

Discrete-Time Systems

FINITE PRECISION EFFECTS 1. FLOATING POINT VERSUS FIXED POINT 3. TYPES OF FINITE PRECISION EFFECTS 4. FACTORS INFLUENCING FINITE PRECISION EFFECTS

EE 3054: Signals, Systems, and Transforms Spring A causal discrete-time LTI system is described by the equation. y(n) = 1 4.

Hilbert Transformator IP Cores

Quadrature-Mirror Filter Bank

APPLIED SIGNAL PROCESSING

III. Time Domain Analysis of systems

Filter structures ELEC-E5410

Transform Analysis of Linear Time-Invariant Systems

Z Transform (Part - II)

2. CONVOLUTION. Convolution sum. Response of d.t. LTI systems at a certain input signal

Z-Transform. x (n) Sampler

Transform Representation of Signals

FILTER DESIGN FOR SIGNAL PROCESSING USING MATLAB AND MATHEMATICAL

8. z-domain Analysis of Discrete-Time Signals and Systems

EEL3135: Homework #4

DSP Configurations. responded with: thus the system function for this filter would be

Digital Wideband Integrators with Matching Phase and Arbitrarily Accurate Magnitude Response (Extended Version)

Cast of Characters. Some Symbols, Functions, and Variables Used in the Book

Let H(z) = P(z)/Q(z) be the system function of a rational form. Let us represent both P(z) and Q(z) as polynomials of z (not z -1 )

7.17. Determine the z-transform and ROC for the following time signals: Sketch the ROC, poles, and zeros in the z-plane. X(z) = x[n]z n.

DIGITAL SIGNAL PROCESSING UNIT III INFINITE IMPULSE RESPONSE DIGITAL FILTERS. 3.6 Design of Digital Filter using Digital to Digital

Analysis of Finite Wordlength Effects

ELEG 305: Digital Signal Processing

University of Illinois at Urbana-Champaign ECE 310: Digital Signal Processing

x[n] = x a (nt ) x a (t)e jωt dt while the discrete time signal x[n] has the discrete-time Fourier transform x[n]e jωn

ELEN E4810: Digital Signal Processing Topic 2: Time domain

Recursive, Infinite Impulse Response (IIR) Digital Filters:

E : Lecture 1 Introduction

Tunable IIR Digital Filters

There are two main classes of digital lter FIR (Finite impulse response) and IIR (innite impulse reponse).

Universiti Malaysia Perlis EKT430: DIGITAL SIGNAL PROCESSING LAB ASSIGNMENT 5: DIGITAL FILTER STRUCTURES

Final Exam January 31, Solutions

Digital Signal Processing. Outline. Hani Mehrpouyan, The Z-Transform. The Inverse Z-Transform

Advanced Signals and Systems

Transcription:

ELEN E481: Digital Signal Processing Topic 7: Filter tpes and structures 1. Some filter tpes 2. Minimum and maximum phase 3. Filter implementation structures 1 1. Some Filter Tpes We have seen the basics of filters and a range of simple examples Now look at a couple of other classes: Comb filters - multiple pass/stop bands Allpass filters - onl modif signal phase 2

Comb Filters Replace all sstem delas with longer delas z -L x[n] z -L z -L [n] z -L z -L Sstem that behaves the same at a longer timescale 3 Comb Filters Parent filter impulse response h[n] becomes comb filter output as: g[n] = {h[] h[1] h[2]..} Thus, Gz L-1 zeros n (= gn [ ]z n = hn [ ]z nl n = Hz ( L 4

Comb Filters Thus, frequenc response: Ge ( jω = He ( jωl 2 parent frequenc response compressed & repeated L times H(e jω G(e jω 2 15 1 5 15 1 5 -π -π/2 π/2 π High-pass response pass ω = π/l, 3π/L, 5π/L... cut ω =, 2π/L, 4π/L... -π -π/2 π/2 π L copies of H(e jω useful to remove a harmonic series 5 Allpass Filters Allpass filter has A(e jω 2 = K ω i.e. spectral energ is not changed Phase response is not zero (else trivial phase correction special effects e.g. Magnitude (db 5-5 -1-15 -2.1.2.3.4.5.6.7.8.9 1 Normalized Frequenc (π rad/sample Phase (degrees -2-4 -6-8.1.2.3.4.5.6.7.8.9 1 Normalized Frequenc (π rad/sample 6

Allpass Filters A M Allpass has special form of sstem fn: (=± z d M d M 1 z 1... d 1 z ( M 1 z M 1 d 1 z 1... d M 1 z ( M 1 d M z M =±z M D M D M ( z 1 ( z A M (z has poles λ where D M (λ = A M (z has zeros ζ = 1/λ = λ -1 = mirror-image polnomials 7 Allpass Filters A M An (stable D M can be used: reciprocal zeros from D M ( Im{z}.5 Phase is alwas decreasing: -Mπ at ω = π -.5-1 (=±z z D M M -3-2 -1 1 D M ( z 1 ( z poles from 1/D M (z 3π.2.4.6.8 /π 8 arg{h(z} π 2π Re{z} peak group dela M

Allpass Filters Wh do mirror-img pol s give const gain? Conj-sm sstem fn can be factored as: A M * ( 1 i (= z K z λ i z λ i i ( i ( = K λ * 1 i z λ * i z 1 z λ i i ( z = e jω = e -jω also on u.circle... 9 ZP complex conjugate p/z λ i λ i * o * λ 1 i e jω e -jω 2. Minimum/Maximum Phase In AP filters, reciprocal roots have.. same effect on magnitude (modulo const. different effect on phase In normal filters, can tr substituting reciprocal roots reciprocal of stable pole will be unstable X reciprocals of zeros? Variants of filters with same magnitude response, different phase 1

Minimum/Maximum Phase Hence: H 1 (z = z - b z - a a H 2 (z = b(z - 1/b z - a a b 1/b reciprocal zero.. 3 3 H(e j 2 1 2 1.. same mag.. (H(e j.5π -.5π -π.2.4.6.8 /π.5π -.5π -π.2.4.6.8 /π.. added phase lag 11 Minimum/Maximum Phase For a given magnitude response All zeros inside u.circle minimum phase All zeros outside u.c. maximum phase (greatest phase dispersion for that order Otherwise, mixed phase i.e. for a given magnitude response several filters & phase fns are possible; minimum phase is canonical, best 12

Minimum/Maximum Phase Note: Min. phase Allpass o o o = Max. phase o polezero cancl n ( z ζ ( z ζ * z λ ( * x = o ( z 1 ζ z 1 ζ ( z ζ z ζ * ( o ( z 1 ζ z 1 ζ ( * z λ 13 Inverse Sstems h i [n] is called the inverse of h f [n] iff h i [ n] h f [ n]= δ[ n] Z-transform: H f ( e jω H i ( e jω =1 x[n] [n] w[n] H f (z H i (z Wz (= H i (Y z (= z H i (H z f wn [ ]= xn [ ] (Xz z (= Xz ( i.e. H i (z recovers x[n] from o/p of H f (z 14

Inverse Sstems What is H i (z? H i (=1/H z f z H i (z is reciprocal polnomial of H f (z H f (= z Pz Dz Just swap poles and zeros: H i z (H f ( ( H i (= z Dz ( Pz ( H f (z (=1 z ( poles of fwd zeros of bwd zeros of fwd poles of bwd 15 o o H i (z o Inverse Sstems When does H i (z exist? Causalstable all H i (z poles inside u.c. all zeros of H f (z must be inside u.c. H f (z must be minimum phase H f (z zeros outside u.c. unstable H i (z H f (z zeros on u.c. unstable H i (z H i ( e jω =1/H f ( e jω lose... =1/ ω=ζ ω onl invert if min.phase, H f (e jω 16

Sstem Identification Inverse filtering = given and H, find x Sstem ID = given (and ~x, find H Just run convolution backwards? n [ ]= hk [ ]xn [ k] x[n] k= H(z [ ]= h[ ]x 1 []= h [ ]x 1 [n] [ ] h[ ] [] h[]x 1 [ ] h 1 deconvolution but: errors accumulate []... 17 Sstem Identification x[n] H? (z [n] Better approach uses correlations; Cross-correlate input and output: r x []= l [] l x[ l]= h? [] l x[] l x l = h? [] l r xx [] l If r xx is simple, can recover h? [n]... e.g. (pseudo- white noise: r xx [] l δ[] l h? [ n] r x l [] [ ] 18

Sstem Identification Can also work in frequenc domain: S x z (= H? ( z S xx z ( make a const. x[n] is not observable S x unavailable, but S xx (e jω ma still be known, so: S ( e jω = Ye ( jω Y * ( e jω = He ( jω Xe ( jω H * ( e jω X * ( e jω = He jω ( 2 S xx ( e jω Use e.g. min.phase to rebuild H(e jω... 19 3. Filter Structures Man different implementations, representations of same filter Different costs, speeds, laouts, noise performance,... 2

x[n] Block Diagrams Useful wa to illustrate implementations Z-transform helps analsis: [n] Y(= z G 1 (Xz z [ ( G 2 (Y z ( z ] G 1 (z Y(1 z [ G 1 (G z 2 ( z ]= G 1 (Xz z G 2 (z Approach Hz (= Y ( z Xz ( = G 1 ( z 1 G 1 (G z 2 ( z Output of summers as dumm variables Everthing else is just multiplicative ( 21 Block Diagrams More complex example: w x[n] 1 w 2 -α β -δ ε [n] z γ -1 W 1 = X αz 1 W 3 W 2 = W 1 δz 1 W 2 w 3 W 3 = z 1 W 2 εw 2 Y = γz 1 W 3 βw 1 W W 2 = 1 1 δz 1 Y X = β z 1 ( βδ γε z 2 ( γ 1 z ( 1 W 3 = z 1 ε ( δ αε z 2 ( α W 1 1 δz 1 stackable 2nd order section 22

Dela-Free Loops Can t have them! x Β Α u v ( u = x ( ( = Bv Au = Bv Ax At time n =, setup inputs x and v ; need u for, also for u can t calculate Algebra: ( 1 BA= Bv BAx Bv BAx = 1 BA 23 x BA 1 BA 1 1 BA B 1 BA u B 1 BA v can simplif... Equivalent Structures Modifications to block diagrams that do not change the filter e.g. Commutation H = AB = BA A B B A Factoring ABCB = (AC B x 1 x 2 A(zB(z C(zB(z x 1 x 2 A(z C(z B(z fewer blocks less computation 24

Equivalent Structures Transpose reverse paths adders nodes input output Y = b 1 X b 2 z 1 X b 3 z 2 X ( = b 1 X z 1 b 2 X z 1 b 3 X x b 1 b 2 b 3 b 1 b 2 b 3 x 25 x x FIR Filter Structures Direct form Tapped Dela Line n [ ]= h xn [ ] h 1 xn 1 [ ]... h h 1 h 2 h 3 h 4 4 = h k xn [ k ] k= Transpose h 4 h 3 h 2 h 1 h Re-use dela line if several inputs x i for single output? 26

FIR Filter Structures Cascade factored into e.g. 2nd order sections Hz (= h h 1 z 1 h 2 z 2 h 3 z 3 = h ( 1 ζ z 1 1 ζ ( 1 z 1 1 ζ ( * 1 z 1 = h ( 1 ζ z 1 1 2Re{ ζ 1 }z 1 ζ 2 1 z 2 x h ζ ( 27-2Re{ζ 1 } ζ 1 2 FIR Filter Structures Linear Phase: Smmetric filters with h[n] = (-h[n - n] ( ( [ ] n [ ]= b xn [ ] xn [ 4] b 1 xn 1 [ ] xn 3 b 2 xn 2 [ ] Also Transpose form: gains first, feeding folded dela/sum line 28 x n b b 1 b 2... half as man multiplies

IIR Filter Structures IIR: numerator denominator Hz (= p p 1 z 1 p 2 z 2... 1 d 1 z 1 d 2 z 2... = Pz ( 1 Dz ( FIR p p 1 p 2 -d 1 -d 2 all-pole IIR 29 IIR Filter Structures Hence, Direct form I Commutation Direct form II (DF2 -d 1 -d 2 p p 1 p 2 -d 1 -d 2 p p 1 p 2 same signal dela lines merge canonical = min. memor usage 3

IIR Filter Structures Use Transpose on FIR/IIR/DF2 x p p 1 p 2 -d 1 -d 2 Direct Form II Transpose 31 real root Factored IIR Structures Real-output filters have α conjugate-smm roots: β 1 Hz (= ( 1 (α jβz 1 1 (α jβz 1 ( Can alwas group into 2nd order terms with real coefficients: Hz (= p ( 1 γ 1 z 1 1 ( 2γ 2 z 1 (γ 2 2 δ 2 2 z 2... ( 1 α 1 z 1 1 ( 2α 2 z 1 (α 2 2 β 2 2 z 2... 32 β

Cascade IIR Structure Implement as cascade of second order sections (in DFII fwd gain factored out x p α 1 -γ 1 2α 2-2γ 2 (α 22 β 22 γ 22 δ 2 2 Second order sections (SOS: modular - an order from optimized block well-behaved, real coefficients (sensitive? 33 Second-Order Sections Free choices: grouping of pole pairs with zero pairs order of sections Optimize numerical properties: avoid ver large values (overflow avoid ver small values (quantization e.g. Matlab s zp2sos attempt to put close roots in same section intersperse gain & attenuation? 34

Second Order Sections Factorization affects intermediate values Original Sstem (2 pair poles, zeros Im{z} H(z / db 1-1 2-2 -1 1 2 Re{z} -2.2.4.6.8 /π 2 H(z / db H(z / db -2.2.4.6.8 /π 2 Factorization 1 Factorization 2 H(z / db H(z / db 2-2.2.4.6.8 /π 2-2.2.4.6.8 /π -2.2.4.6.8 /π 35 Parallel IIR Structures Can express H(z as sum of terms (IZT H(z = consts ρ l l=1 1 λ l z 1 Or, second-order terms: γ H(z = γ k γ 1k z 1 k 1 α 1k z 1 α 2k z 2 N ρ l = ( 1 λ l z 1 Fz ( z=λl Suggests parallel realization... 36

x Parallel IIR Structures -α 11 -α 21 -α 12 -α 22 γ γ 1 γ 11 γ 2 γ 12 Sum terms become parallel paths Poles of each SOS are from full TF Sstem zeros arise from output sum Wh do this? stabilit/sensitivit reuse common terms 37