Digital Signal Processing and Digital Filter
Design (Draft)
By:
C. Sidney Burrus
Digital Signal Processing and Digital Filter
Design (Draft)
By:
C. Sidney Burrus
Online:
C O N N E X I O N S
Rice University, Houston, Texas
©2008 C. Sidney Burrus
This selection and arrangement of content is licensed under the Creative Commons Attribution License:
http://creativecommons.org/licenses/by/2.0/
Table of Contents
Preface: Digital Signal Processing and Digital Filter Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1 Signals and Signal Processing Systems
1.1 Continuous-Time Signals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2 Discrete-Time Signals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
1.3 Discrete-Time Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
1.4 Sampling, UpSampling, DownSampling, and MultiRate . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . 31
Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ??
2 Finite Impulse Response Digital Filters and Their Design
2.1 FIR Digital Filters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
2.2 FIR Filter Design by Frequency Sampling or Interpolation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
2.3 Least Squared Error Design of FIR Filters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
2.4 Chebyshev or Equal Ripple Error Approximation Filters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
2.5 Taylor Series, Maximally Flat, and Zero Moment Design Criteria . . . . . . . . . . . . . . . . . . . . . . . . . . 107
2.6 Constrained Approximation and Mixed Criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ??
3 Innite Impulse Response Digital Filters and Their Design
3.1 Properties of IIR Filters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129
3.2 Design of Innite Impulse Response (IIR) Filters by Frequency Transformations . . . . . . . . . . . . 132
3.3 Butterworth Filter Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135
3.4 Chebyshev Filter Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141
3.5 Elliptic-Function Filter Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151
3.6 Optimality of the Four Classical Filter Designs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164
3.7 Frequency Transformations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164
3.8 Conversion of Analog to Digital Transfer Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169
3.9 Direct Frequency Domain IIR Filter Design Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179
Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ??
4 Digital Filter Structures and Implementation
4.1 Block, Multi-rate, Multi-dimensional Processing and Distributed Arithmetic . . . . . . . . . . . . . . . 187
Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ??
Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193
Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 214
Attributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .215
iv
Preface: Digital Signal Processing and
Digital Filter Design1
Digital signal processing (DSP) has existed as long as quantitative calculations have been systematically
applied to data in Science, Social Science, and Technology. The set of activities started out as a collection
of ideas and techniques in very dierent applications. Around 1965, when the fast Fourier transform (FFT)
was rediscovered, DSP was extracted from its applications and became a single academic and professional
discipline to be developed as far as possible.
One of the earliest books on DSP was by Gold and Rader [125], written in 1968, although there had
been earlier books on sampled data control and time series analysis, and chapters in books on computer
applications.
In the late 60's and early 70's there was an explosion of activity in both the theory and
application of DSP. As the area was beginning to mature, two very important books on DSP were published
in 1975, one by Oppenheim and Schafer [225] and the other by Rabiner and Gold [284]. These three books
dominated the early courses in universities and self study in industry.
The early applications of DSP were in the defense, oil, and medical industries. They were the ones who
needed and could aord the expensive but higher quality processing that digital techniques oered over
analog signal processing. However, as the theory developed more ecient algorithms, as computers became
more powerful and cheaper, and nally, as DSP chips became commodity items (e.g.
the Texas Instru-
ments TMS-320 series) DSP moved into a variety of commercial applications and the current digitization
of communications began. The applications are now everywhere. They are tele-communications, seismic
signal processing, radar and sonar signal processing, speech and music signal processing, image and picture
processing, entertainment signal processing, nancial data signal processing, medical signal processing, non-
destructive testing, factory oor monitoring, simulation, visualization, virtual reality, robotics, and control.
DSP chips are found in virtually all cell phones, digital cameras, high-end stereo systems, MP3 players, DVD
players, cars, toys, the Segway", and many other digital systems.
In a modern curriculum, DSP has moved from a specialized graduate course down to a general undergrad-
uate course, and, in some cases, to the introductory freshman or sophomore EE course [198]. An exciting
project is experimenting with teaching DSP in high schools and in colleges to non-technical majors [237].
Our reason for writing this book and adding to the already long list of DSP books is to cover the new
results in digital lter design that have become available in the last 10 to 20 years and to make these results
available on line in Connexions as well as print. Digital lters are important parts of a large number of
systems and processes. In many cases, the use of modern optimal design methods allows the use of a less
expensive DSP chip for a particular application or obtaining higher performance with existing hardware. The
book should be useful in an introductory course if the students have had a course on discrete-time systems.
It can be used in a second DSP course on lter design or used for self-study or reference in industry.
We rst cover the optimal design of Finite Impulse Response (FIR) lters using a least squared error, a
maximally at, and a Chebyshev criterion. A feature of the book is covering nite impulse response (FIR)
lter design before innite impulse response (IIR) lter design. This reects modern practice and new lter
design algorithms. The FIR lter design chapter contains new methods on constrained optimization, mixed
1This content is available online at .
1
2
optimization criteria, and modications to the basic Parks-McClellan algorithm that are very useful. Design
programs are given in MatLab and FORTRAN.
A brief chapter on structures and implementation presents block processing for both FIR and IIR lters,
distributed arithmetic structures for multiplierless implementation, and multirate systems for lter banks
and wavelets. This is presented as a generalization to sampling and to periodically time-varying systems.
The bifrequency map gives a clearer explanation of aliasing and how to control it.
The basic notes that were developed into this book have evolved over 35 years of teaching and conducting
research in DSP at Rice, Erlangen, and MIT. They contain the results of research on lters and algorithms
done at those universities and other universities and industries around the world. The book tries to give
not only the dierent methods and approaches, but also reasons and intuition for choosing one method over
another. It should be interesting to both the university student and the industrial practitioner.
We want to acknowledge with gratitude the long time support of Texas Instruments, Inc., the National
Science Foundation, National Instruments, Inc. and the MathWorks, Inc. as well as the support of the
Maxeld and Oshman families. We also want to thank our long-time colleagues Tom Parks, Hans Schuessler,
Jim McClellan, Al Oppenheim, Sanjit Mitra, Ivan Selesnick, Doug Jones, Don Johnson, Leland Jackson, Rich
Baraniuk, and our graduate students over 30 years from whom we have learned much and with whom we have
argued often, particularly, Selesnick, Gopinath, Soewito, and Vargas. We also owe much to the IEEE Signal
Processing Society and to Rice University for environments to learn, teach, create, and collaborate. Much of
the results in DSP was supported directly or indirectly by the NSF, most recently NSF grant EEC-0538934
in the Partnerships for Innovation program working with National Instruments, Inc.
We particularly thank Texas Instruments and Prentice Hall for returning the copyrights to me so that part
of the material in DFT/FFT and Convolution Algorithms[58], Design of Digital Filters[245], and
Ecient Fourier Transform and Convolution Algorithms" in Advanced Topics in Signal Processing[44]
could be included here under the Creative Commons Attribution copyright. I also appreciate IEEE policy
that allows parts of my papers to be included here.
A rather long list of references is included to point to more background, to more advanced theory, and to
applications. A book of Matlab DSP exercises that could be used with this book has been published through
Prentice Hall [56], [199]. Some Matlab programs are included to aid in understanding the design algorithms
and to actually design lters. LabView from National Instruments is a very useful tool to both learn with
and use in application. All of the material in these notes is being put into Connexions" [22] which is a
modern web-based open-content information system www.cnx.org. Further information is available on our
web site at www.dsp.rice.edu with links to other related work. We thank Richard Baraniuk, Don Johnson,
Ray Wagner, Daniel Williamson, and Marcia Horton for their help.
This version of the book is a draft and will continue to evolve under Connexions. A companion FFT
book is being written and is also available in Connexions and print form. All of these two books are in
the repository of Connexions and, therefore, available to anyone free to use, reuse, modify, etc. as long as
attribution is given.
C. Sidney Burrus
Houston, Texas
2008/06/10 10:23:04
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