Digital Signal Processing P1

Contents PART I Signals and Systems 1 Fourier Series, Fourier Transforms, and the DFT W. Kenneth Jenkins 2 Ordinary Linear Differential and Difference Equations B.P. Lathi 3 Finite Wordlength Effects Bruce W. Bomar PART II Signal Representation and Quantization 4 On Multidimensional Sampling Ton Kalker 5 Analog-to-Digital Conversion Architectures Stephen Kosonocky and Peter Xiao 6 Quantization of Discrete Time Signals Ravi P. Ramachandran PART III Fast Algorithms and Structures 7 Fast Fourier Transforms: A Tutorial Review and a State of the Art P. Duhamel and M. Vetterli 8 Fast Convolution and Filtering Ivan W. Selesnick and C. Sidney Burrus 9 Complexity Theory of Transforms in Signal Processing Ephraim Feig 10 Fast Matrix Computations Andrew E. Yagle 11 Digital Filtering Lina J. Karam, James H. McClellan, Ivan W. Selesnick, and C. Sidney Burrus PART V Statistical Signal Processing 12 Overview of Statistical Signal Processing Charles W. Therrien 13 Signal Detection and Classification Alfred Hero 14 Spectrum Estimation and Modeling Petar M. Djuri´c and Steven M. Kay 15 Estimation Theory and Algorithms: From Gauss to Wiener to Kalman JerryM.Mendel 16 Validation, Testing, and Noise Modeling Jitendra K. Tugnait 17 Cyclostationary Signal Analysis Georgios B. Giannakis PART VI Adaptive Filtering 18 Introduction to Adaptive Filters Scott C. Douglas 19 Convergence Issues in the LMS Adaptive Filter Scott C. Douglas and Markus Rupp 20 Robustness Issues in Adaptive Filtering Ali H. Sayed and Markus Rupp 21 Recursive Least-Squares Adaptive Filters Ali H. Sayed and Thomas Kailath 22 Transform Domain Adaptive Filtering W. Kenneth Jenkins and Daniel F. Marshall 23 Adaptive IIR Filters Geoffrey A. Williamson 24 Adaptive Filters for Blind Equalization Zhi Ding c  1999 by CRC Press LLC PART VII Inverse Problems and Signal Reconstruction 25 Signal Recovery from Partial Information Christine Podilchuk 26 Algorithms for Computed Tomography Gabor T. Herman 27 Robust Speech Processing as an Inverse Problem Richard J. Mammone and Xiaoyu Zhang 28 Inverse Problems, Statistical Mechanics and Simulated Annealing K. Venkatesh Prasad 29 Image Recovery Using the EM Algorithm Jun Zhang and Aggelos K. Katsaggelos 30 Inverse Problems in Array Processing KevinR.Farrell 31 Channel Equalization as a Regularized Inverse Problem John F. Doherty 32 Inverse Problems in Microphone Arrays A.C. Surendran 33 Synthetic Aperture Radar Algorithms Clay Stewart and Vic Larson 34 Iterative Image Restoration Algorithms Aggelos K. Katsaggelos PART VIII Time Frequency and Multirate Signal Processing 35 Wavelets and Filter Banks Cormac Herley 36 Filter Bank Design Joseph Arrowood, Tami Randolph, and Mark J.T. Smith 37 Time-Varying Analysis-Synthesis Filter Banks Iraj Sodagar 38 Lapped Transforms Ricardo L. de Queiroz PART IX Digital Audio Communications 39 Auditory Psychophysics for Coding Applications Joseph L. Hall 40 MPEG Digital Audio Coding Standards Peter Noll 41 Digital Audio Coding: Dolby AC-3 Grant A. Davidson 42 The Perceptual Audio Coder (PAC) Deepen Sinha, James D. Johnston, Sean Dorward, and Schuyler R. Quackenbush 43 Sony Systems Kenzo Akagiri, M.Katakura, H. Yamauchi, E. Saito, M. Kohut, Masayuki Nishiguchi, and K. Tsutsui PART X Speech Processing 44 Speech Production Models and Their Digital Implementations M. Mohan Sondhi and Juergen Schroeter 45 Speech Coding RichardV.Cox 46 Text-to-Speech Synthesis Richard Sproat and Joseph Olive 47 Speech Recognition by Machine Lawrence R. Rabiner and B. H. Juang 48 Speaker Verification Sadaoki Furui and Aaron E. Rosenberg 49 DSP Implementations of Speech Processing Kurt Baudendistel 50 Software Tools for Speech Research and Development John Shore PART XI Image and Video Processing 51 Image Processing Fundamentals Ian T. Young, Jan J. Gerbrands, and Lucas J. van Vliet 52 Still Image Compression Tor A. Ramstad 53 Image and Video Restoration A. Murat Tekalp 54 Video Scanning Format Conversion and Motion Estimation Gerard de Haan c  1999 by CRC Press LLC 55 Video Sequence Compression Osama Al-Shaykh, Ralph Neff, David Taubman, and Avideh Zakhor 56 Digital Television Kou-Hu Tzou 57 Stereoscopic Image Processing Reginald L. Lagendijk, Ruggero E.H. Franich, and Emile A. Hendriks 58 A Survey of Image Processing Software and Image Databases Stanley J. Reeves 59 VLSI Architectures for Image Communications P. Pirsch and W. Gehrke PART XII Sensor Array Processing 60 Complex Random Variables and Stochastic Processes Daniel R. Fuhrmann 61 Beamforming Techniques for Spatial Filtering Barry Van Veen and Kevin M. Buckley 62 Subspace-Based Direction Finding Methods Egemen Gonen and Jerry M. Mendel 63 ESPRIT and Closed-Form 2-D Angle Estimation with Planar Arrays Martin Haardt, Michael D. Zoltowski, Cherian P. Mathews, and Javier Ramos 64 A Unified Instrumental Variable Approach to Direction Finding in Colored Noise Fields P. Stoica, M. Viberg, M. Wong, and Q. Wu 65 Electromagnetic Vector-Sensor Array Processing Arye Nehorai and Eytan Paldi 66 Subspace Tracking R.D. DeGroat, E.M. Dowling, and D.A. Linebarger 67 Detection: Determining the Number of Sources Douglas B. Williams 68 Array Processing for Mobile Communications A. Paulraj and C. B. Papadias 69 Beamforming with Correlated Arrivals in Mobile Communications Victor A.N. Barroso and Jos´e M.F. Moura 70 Space-Time Adaptive Processing for Airborne Surveillance Radar Hong Wang PART XIII Nonlinear and Fractal Signal Processing 71 Chaotic Signals and Signal Processing Alan V. Oppenheim and Kevin M. Cuomo 72 Nonlinear Maps Steven H. Isabelle and Gregory W. Wornell 73 Fractal Signals Gregory W. Wornell 74 Morphological Signal and Image Processing Petros Maragos 75 Signal Processing and Communication with Solitons Andrew C. Singer 76 Higher-Order Spectral Analysis Athina P. Petropulu PART XIV DSP Software and Hardware 77 Introduction to the TMS320 Family of Digital Signal Processors Panos Papamichalis 78 Rapid Design and Prototyping of DSP Systems T. Egolf, M. Pettigrew, J. Debardelaben, R. Hezar, S. Famorzadeh, A. Kavipurapu, M. Khan, Lan-Rong Dung, K. Balemarthy, N. Desai, Yong-kyu Jung, and V. Madisetti c  1999 by CRC Press LLC To our families c  1999 by CRC Press LLC Preface Digital Signal Processing (DSP) is concerned with the theoretical and practical aspects of representing information bearing signals in digital form and with using computers or special purpose digital hardware either to extract that information or to transform the signals in useful ways. Areas where digital signal processing has made a significant impact include telecommunications, man-machine communications, computer engineering, multimedia applications, medical technology, radar and sonar, seismic data analysis, and remote sensing, to name just a few. During thefirstfifteenyearsof itsexistence, the fieldof DSPsawadvancementsinthe basictheory of discrete-timesignals and processing tools. This work included suchtopics as fast algorithms, A/D and D/A conversion, and digital filter design. The past fifteen years has seen an ever quickening growth of DSP in application areas such as speech and acoustics, video, radar, and telecommunications. Much of this interest in using DSP has been spurred on by developments in computer hardware and microprocessors. Digital Signal Processing Handbook CRCnetBASE is an attempt to capture the entire range of DSP: from theory to applications — from algorithms to hardware. Given the widespread use of DSP, a need developed for an authoritative reference, written by some ofthe topexperts in the world. This needwas toprovide information onboth theoreticaland practical issues suitable for a broad audience — ranging from professionals in electrical engineering, computer science, and relatedengineering fields, to managers involved indesign and marketing, and to graduate students and scholars in the field. Given the large number of excellent introductory texts in DSP, it was also important to focus on topics useful to the engineer or scholar without overemphasizing those aspects that are already widely accessible. In short, we wished to create a resource that was relevant to the needs of the engineering community and that will keep them up-to-date in the DSP field. A task of this magnitude was only possible through the cooperation of many of the foremost DSP researchers and practitioners. This collaboration, over the past threeyears, has resultedin a CD-ROM containing a comprehensive range of DSP topics presented with a clarity of vision and a depth of coverage that is expected to inform, educate, and fascinate the reader. Indeed, many of the articles, written by leaders in their fields, embody unique visions and perceptions that enable a quick, yet thorough, exposure to knowledge garnered over years of development. As with other CRC Press handbooks, we have attempted to provide a balance between essential information, background material, technical details, and introduction to relevant standards and software. The Handbook pays equal attention to theory, practice, and application areas. Digital Signal Processing Handbook CRCnetBASE can be used in a number of ways. Most users will look up a topic of interest by using the powerful search engine and then viewing the applicable chapters. As such, each chapter has been written to stand alone and give an overview of its subject matter while providing key references for those interested in learning more. Digital Signal Processing Handbook CRCnetBASE can also be used as a reference book for graduate classes, or as supporting material for continuing education courses in the DSP area. Industrial organizations may wish to provide the CD-ROM with their products to enhance their value by providing a standard and up-to-date reference source. Wehave beenvery impressedwith the quality ofthis work, whichis dueentirelyto thecontributions of all the authors, and we would like to thank them all. The Advisory Board was instrumental in helping to choose subjects and leaders for all the sections. Being experts in their fields, the section leaders provided the vision and fleshed out the contents for their sections. c  1999 by CRC Press LLC Finally, the authors produced the necessary content for this work. To them fell the challenging task of writing for such a broad audience, and they excelled at their jobs. In addition to these technical contributors, we wish to thank a number of outstanding individuals whose administrative skills made this project possible. Without the outstanding organizational skills of Elaine M. Gibson, this handbook may never have been finished. Not only did Elaine manage the paperwork, but she had the unenviable task of reminding authors about deadlines and pushing them to finish. We also thank a number of individuals associated with the CRC Press Handbook Series over a period of time, especially Joel Claypool, Dick Dorf, Kristen Maus, Jerry Papke, Ron Powers, Suzanne Lassandro, and Carol Whitehead. We welcome you to this handbook, and hope you find it worth your interest. Vijay K. Madisetti and Douglas B. Williams Center for Signal and Image Processing School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta, Georgia c  1999 by CRC Press LLC Editors Vijay K. Madisetti is an Associate Professor in the School of Electrical and Computer Engineering at Georgia Institute of Technology in Atlanta. He teaches undergraduate and graduate courses in signal processing and computer engineering, and is affiliated with the Center for Signal and Image Processing (CSIP) and the Microelectronics Research Center (MiRC) on campus. He received his B. Tech (honors) from the Indian Institute of Technology (IIT), Kharagpur, in 1984, and his Ph.D. from the University of California at Berkeley, in 1989, in electrical engineering and computer sciences. Dr. Madisetti is active professionally in the area of signal processing, having served as an Associate Editor of the IEEE Transactions on Circuits and Systems II, the International Journal in Computer Simulation, and the Journal of VLSI Signal Processing. He has authored, co-authored, or edited six books in the areas of signal processing and computer engineering, including VLSI Digital Signal Processors (IEEE Press, 1995), Quick-Turnaround ASIC Design in VHDL (Kluwer, 1996), and a CD- ROM tutorial on VHDL (IEEE Standards Press, 1997). He serves as the IEEE Press Signal Processing Society liaison, and is counselor to Georgia Tech’s IEEE Student Chapter, which is one of the largest in the world with over 600 members in 1996. Currently, he is serving as the Technical Director of DARPA’s RASSP Education and Facilitation program, a multi-university/industry effort to develop a new digital systems design education curriculum. Dr. Madisetti is a frequent consultant to industry and the U.S. government, and also serves as the President and CEO of VP Technologies, Inc., Marietta, GA., a corporation that specializes in rapid prototyping, virtual prototyping, and design of embedded digital systems. Dr. Madis- etti’s home page URL is at http://www.ee.gatech.edu/users/215/index.html, and he can be reached at vkm@ee.gatech.edu. c  1999 by CRC Press LLC Editors Douglas B. Williams received the B.S.E.E. degree (summa cum laude), the M.S. degree, and the Ph.D. degree, in electrical and computer engineering from Rice University, Houston, Texas in 1984, 1987, and 1989, respectively. In 1989, he joined the faculty of the School of Electrical and Computer Engineering at the Georgia Institute of Technology, Atlanta, Georgia, where he is currently an Associate Professor. There he is also affiliated with the Center for Signal and Image Processing (CSIP) and teaches courses in signal processing and telecommunications. Dr. Williams has served as an Associate Editor of the IEEE Transactions on Signal Processing and was on the conference committee for the 1996 International Conference on Acoustics, Speech, and Signal Processing that was held in Atlanta. He is currently the faculty counselor for Georgia Tech’s student chapter of the IEEE Signal Processing Society. He is a member of the Tau Beta Pi, Eta Kappa Nu, and Phi Beta Kappa honor societies. Dr. Williams’s current research interests are in statistical signal processing with emphasis on radar signal processing, communications systems, and chaotic time-series analysis. More information on his activities may be found on his home page at http://dogbert.ee.gatech.edu/users/276. He can also be reached at dbw@ee.gatech.edu. c  1999 by CRC Press LLC I SignalsandSystems VijayK.Madisetti GeorgiaInstituteofTechnology DouglasB.Williams GeorgiaInstituteofTechnology 1FourierSeries,FourierTransforms,andtheDFT W.KennethJenkins Introduction • FourierSeriesRepresentationofContinuousTimePeriodicSignals • TheClassical FourierTransformforContinuousTimeSignals • TheDiscreteTimeFourierTransform • The DiscreteFourierTransform • FamilyTreeofFourierTransforms • SelectedApplicationsofFourier Methods • Summary 2OrdinaryLinearDifferentialandDifferenceEquations B.P.Lathi DifferentialEquations • DifferenceEquations 3FiniteWordlengthEffects BruceW.Bomar Introduction • NumberRepresentation • Fixed-PointQuantizationErrors • Floating-PointQuan- tizationErrors • RoundoffNoise • LimitCycles • OverflowOscillations • CoefficientQuantization Error • RealizationConsiderations T HESTUDYOF“SIGNALSANDSYSTEMS”hasformedacornerstoneforthedevelopmentof digitalsignalprocessingandiscrucialforallofthetopicsdiscussedinthisHandbook.While thereaderisassumedtobefamiliarwiththebasicsofsignalsandsystems,asmallportionis reviewedinthischapterwithanemphasisonthetransitionfromcontinuoustimetodiscretetime. Thereaderwishingmorebackgroundmayfindinitanyofthemanyfinetextbooksinthisarea,for example[1]-[6]. Inthechapter“FourierSeries,FourierTransforms,andtheDFT”byW.KennethJenkins,many importantFouriertransformconceptsincontinuousanddiscretetimearepresented.Thediscrete Fouriertransform(DFT),whichformsthebackboneofmoderndigitalsignalprocessingasitsmost commonsignalanalysistool,isalsodescribed,togetherwithanintroductiontothefastFourier transformalgorithms. In“OrdinaryLinearDifferentialandDifferenceEquations”,theauthor,B.P.Lathi,presentsa detailedtutorialofdifferentialanddifferenceequationsandtheirsolutions.Becausetheseequations arethemostcommonstructuresforbothimplementingandmodellingsystems,thisbackgroundis necessaryfortheunderstandingofmanyofthelatertopicsinthisHandbook.Ofparticularinterest areanumberofsolvedexamplesthatillustratethesolutionstotheseformulations. c  1999byCRCPressLLC [...]... considerations for realizations of digital signal processing applications, with a special emphasis on filtering References [1] Jackson, L.B., Signals, Systems, and Transforms, Addison-Wesley, Reading, MA, 1991 [2] Kamen, E.W and Heck, B.S., Fundamentals of Signals and Systems Using MATLAB, Prentice-Hall, Upper Saddle River, NJ, 1997 [3] Oppenheim, A.V and Willsky, A.S., with Nawab, S.H., Signals and Systems, 2nd... Manolakis, D.G., Introduction to Digital Signal Processing, Macmillan, New York; Collier Macmillan, London, 1988 [6] Oppenheim, A.V and Schafer, R.W., Discrete Time Signal Processing, Prentice-Hall, Englewood Cliffs, NJ, 1989 c 1999 by CRC Press LLC 1 Fourier Series, Fourier Transforms, and the DFT 1.1 1.2 Introduction Fourier Series Representation of Continuous Time Periodic Signals Exponential Fourier... LLC as a basis for digital signal processing (DSP) because it extends the theory of classical Fourier analysis to DT signals and leads to many effective algorithms that can be directly implemented on general computers or special purpose DSP devices The relationship between the CT and the DT domains is characterized by the operations of sampling and reconstruction If sa (t) denotes a signal s(t) that... series is common in the signal processing literature because it replaces complex coefficients with real ones and often results in a simpler and more intuitive interpretation of the results 1.2.3 Convergence of the Fourier Series The Fourier series representation of a periodic signal is an approximation that exhibits mean squared convergence to the true signal If s(t) is a periodic signal of period T ,... DFFT is useful for the analysis and design of digital filters that are produced by frequency sampling techniques 1.4.2 Relationship between the Continuous and Discrete Time Spectra Because DT signals often originate by sampling CT signals, it is important to develop the relationship between the original spectrum of the CT signal and the spectrum of the DT signal that results First, c 1999 by CRC Press... the (odd) sine terms, and b0 is the DC level of the signal Therefore, if it can be determined by inspection that a signal has DC level, or if it is even or odd, then the correct form of the trigonometric c 1999 by CRC Press LLC series can be chosen to simplify the analysis For example, it is easily seen that the signal shown in Fig 1.5 is an even signal with a zero DC level Therefore it can be accurately... Block Processing in Real-Time Filtering Applications • Fast Fourier Transform Algorithms Family Tree of Fourier Transforms Selected Applications of Fourier Methods Fast Fourier Transform in Spectral Analysis • Finite Impulse Response Digital Filter Design • Fourier Analysis of Ideal and Practical Digital- to-Analog Conversion 1.8 Summary References Introduction Fourier methods are commonly used for signal. .. FIGURE 1.3: Periodic CT signal used in Fourier series example FIGURE 1.4: Magnitude of the Fourier coefficients for example of Figure 1.3 1.2.2 The Trigonometric Fourier Series Although Fourier series expansions exist for complex periodic signals, and Fourier theory can be generalized to the case of complex signals, the theory and results are more easily expressed for realvalued signals The following... results are more easily expressed for realvalued signals The following discussion assumes that the signal s(t) is real-valued for the sake of simplifying the discussion However, all results are valid for complex signals, although the details of the theory will become somewhat more complicated For real-valued signals s(t), it is possible to manipulate the complex exponential form of the Fourier series into... Representation of Continuous Time Periodic Signals It is convenient to begin this discussion with the classical Fourier series representation of a periodic time domain signal, and then derive the Fourier integral from this representation by finding the limit of the Fourier coefficient representation as the period goes to infinity The conditions under which a periodic signal s(t) can be expanded in a Fourier . Adaptive Processing for Airborne Surveillance Radar Hong Wang PART XIII Nonlinear and Fractal Signal Processing 71 Chaotic Signals and Signal Processing. Sidney Burrus PART V Statistical Signal Processing 12 Overview of Statistical Signal Processing Charles W. Therrien 13 Signal Detection and Classification