Signal Processing

James Vincent Candy, PhD, is the Chief Scientist for Engineering, a Distinguished Member of the Technical Staff, founder and former Director of the Center for Advanced Signal & Image Sciences (CASIS) at the Lawrence Livermore National Laboratory and an Adjunct Full-Professor at the University of California, Santa Barbara. He received his his BSEE from the University of Cincinnati along with his MSE and PhD in Electrical Engineering from the University of Florida. Dr. Candy is a Life-Fellow of the IEEE and a 25-Year-Fellow of the Acoustical Society of America (ASA). He was elected as a Life Member at the University of Cambridge (Clare Hall College). Dr. Candy has been awarded the Interdisciplinary Helmholtz-Rayleigh Silver Medal in Signal Processing/Underwater Acoustics by the Acoustical Society of America, the IEEE Distinguished Technical Achievement Award for the development of model-based signal processing as well as an elected IEEE Distinguished Lecturer in Oceanic Signal Processing. He also received the R&D100 award for his innovative invention in radiation threat detection. He has published over 250 journal articles, book chapters, and technical reports as well as written six texts in signal processing: Signal Processing: the Model-Based Approach, (McGraw-Hill, 1986), Signal Processing: the Modern Approach,(McGraw-Hill, 1988), Model-Based Signal Processing, (Wiley/IEEE Press, 2006), Bayesian Signal Processing: Classical, Modern and Particle Filtering (Wiley/IEEE Press, 2009), Bayesian Signal Processing: Classical, Modern and Particle Filtering, 2nd Ed. (Wiley/IEEE Press, 2016), and Model-Based Processing: An Applied Subspace Identification Approach (Wiley, 2019).

• About the Author xiiiPreface xvAcknowledgments xxvGlossary xxviAbout the Companion Website xxx1 Introduction 11.1 Background 11.2 Spectral Decomposition 41.3 Data Decomposition 61.4 Model-based Decomposition 131.5 Notation and Terminology 241.6 Summary 25MATLAB® Notes 26References 26Problems 272 Random Signals and Systems 312.1 Introduction 312.2 Discrete Random Signals 342.3 Spectral Representation of Random Signals 382.4 Discrete Systems with Random Inputs 412.5 Classical Spectral Estimation 442.6 Case Study: Sinusoids in Noise 522.7 Summary 54MATLAB® Notes 55References 55Problems 563 Signal Models 613.1 Data-Based Models 613.2 Parametric-Based Models 653.3 State-space Models 853.4 Summary 103MATLAB® Notes 103References 104Problems 1054 Signal Estimation 1114.1 Classical Estimation 1114.2 Minimum Variance (MV) Estimation 1164.3 Maximum A-Posteriori (MAP) Estimation 1194.4 Maximum Likelihood (ML) Estimation 1214.5 Least-squares (LS) Estimation 1244.6 Optimal Signal Estimation 1334.7 Projection Theory 1374.8 Summary 142MATLAB® Notes 142References 143Problems 1445 Signal Decomposition 1495.1 Introduction 1495.2 Data-Based Decompositions 1495.3 Spectral-Based Decompositions 1685.4 Model-Based Decomposition 1795.5 Case Study: Harmonics in Noise 1985.6 Summary 201MATLAB® Notes 201References 202Problems 2066 Model-based Decomposition: Time Domain 2116.1 Background: State-space Systems 2116.2 Realization Problem 2206.3 Realization Decomposition 2286.4 Subspace Decomposition: Orthogonal Projections 2336.5 Subspace Decomposition: Oblique Projections 2446.6 System Order Estimation and Validation 2536.7 Case Study: Multichannel Mechanical Systems 2596.8 Summary 267MATLAB® Notes 268References 268Problems 2707 Model-Based Decomposition: Frequency Domain 2797.1 Introduction 2797.2 Frequency Response Functions (FRF) 2827.3 Least-squares Complex Frequency (LSCF) Method 2957.4 PolyReference Least-Squares Complex Frequency (pLSCF) Method 3017.5 Maximum Likelihood PolyReference Frequency Domain Estimation (ML-pLSCF) 3077.6 Case Study: 15-DOF Structure 3127.7 Summary 320MATLAB® Notes 322References 322Problems 3248 Performance Analysis 3298.1 Statistical Performance Methods 3298.2 Physical Performance Metrics 3448.3 Case Study: Resonant Modal MCK System 3528.4 Summary 355MATLAB® Notes 355References 3559 Applications 3599.1 Modal Decomposition: Sounding Rocket Flight 3599.2 Vibrational Response of a Cylindrical Structure: Identification and Modal Tracking 3709.3 Resonant Ultrasound Spectroscopy 3779.4 Model-Based Subsystem Decomposition of an 8-Story (8-Mass) Structure 3909.5 Data-Based Decomposition: Time-Reversal Processing 403References 413A Probability and Statistics Overview 417A.1 Probability Theory 417A.2 Gaussian Random Vectors 422A.3 Uncorrelated Transformation: Gaussian Random Vectors 423A.4 Toeplitz Correlation Matrices 424A.5 Important Processes 424References 426B Projection Theory 427B.1 Projections: Deterministic Spaces 427B.2 Projections: Random Spaces 428B.3 Projection: Operators 429B.3.1 Orthogonal (Perpendicular) Projections 429B.3.2 Oblique (Parallel) Projections 430References 432C Matrix Decompositions 433C.1 Singular Value Decomposition 433C.2 QR Decomposition 435C.3 LQ Decomposition 435References 436Index 437