Detection Estimation and Modulation Theory, Part I (inbunden)
Format
Inbunden (Hardback)
Språk
Engelska
Antal sidor
1176
Utgivningsdatum
2013-04-12
Upplaga
2nd Edition
Förlag
John Wiley & Sons Inc
Medarbetare
Tian, Zhi
Illustrationer
Illustrations
Volymtitel
Part I
Dimensioner
254 x 183 x 58 mm
Vikt
2134 g
Antal komponenter
1
Komponenter
,
ISBN
9780470542965
Detection Estimation and Modulation Theory, Part I (inbunden)

Detection Estimation and Modulation Theory, Part I

Detection, Estimation, and Filtering Theory

Inbunden Engelska, 2013-04-12
909
Bokens leverantör håller tillfälligt stängt på grund av Coronapandemin. Klicka "Bevaka" för att få ett mejl när boken går att beställa igen eller välj ett annat format nedan.
Finns även som
Visa alla 2 format & utgåvor
Originally published in 1968, Harry Van Trees s Detection, Estimation, and Modulation Theory, Part I is one of the great time-tested classics in the field of signal processing. Highly readable and practically organized, it is as imperative today for professionals, researchers, and students in optimum signal processing as it was over thirty years ago. The second edition is a thorough revision and expansion almost doubling the size of the first edition and accounting for the new developments thus making it again the most comprehensive and up-to-date treatment of the subject. With a wide range of applications such as radar, sonar, communications, seismology, biomedical engineering, and radar astronomy, among others, the important field of detection and estimation has rarely been given such expert treatment as it is here. Each chapter includes section summaries, realistic examples, and a large number of challenging problems that provide excellent study material. This volume which is Part I of a set of four volumes is the most important and widely used textbook and professional reference in the field.
Visa hela texten

Kundrecensioner

Har du läst boken? Sätt ditt betyg »

Bloggat om Detection Estimation and Modulation Theor...

Övrig information

HARRY L. VAN TREES, ScD., received his BSc. from the United States Military Academy and his ScD. from Massachusetts Institute of Technology. During his fourteen years as a Professor of Electrical Engineering at MIT, he wrote Parts I, II, and III of the DEMT series. On loan from MIT, he served in four senior DoD positions including Chief Scientist of the U.S. Air Force and Principal Deputy Assistant Secretary of Defense (C3I). Returning to academia as an endowed professor at George Mason University, he founded the C3I Center and published Part IV of the DEMT series, Optimum Array Processing. He is currently a University Professor Emeritus. KRISTINE L. BELL, PhD, is a Senior Scientist at Metron, Inc., and an affiliate faculty member in the Statistics Department at George Mason University. She coedited with Dr. Van Trees the Wiley-IEEE book Bayesian Bounds for Parameter Estimation and Nonlinear Filtering/Tracking. ZHI TIAN, PhD, is a Professor of Electrical and Computer Engineering at Michigan Technological University. She is a Fellow of the IEEE.

Innehållsförteckning

Preface xv Preface to the First Edition xix 1 Introduction 1 1.1 Introduction 1 1.2 Topical Outline 1 1.3 Possible Approaches 11 1.4 Organization 14 2 Classical Detection Theory 17 2.1 Introduction 17 2.2 Simple Binary Hypothesis Tests 20 2.2.1 Decision Criteria 20 2.2.2 Performance: Receiver Operating Characteristic 35 2.3 M Hypotheses 51 2.4 Performance Bounds and Approximations 63 2.5 Monte Carlo Simulation 80 2.5.1 Monte Carlo Simulation Techniques 80 2.5.2 Importance Sampling 86 2.5.2.1 Simulation of PF 87 2.5.2.2 Simulation of PM 91 2.5.2.3 Independent Observations 94 2.5.2.4 Simulation of the ROC 94 2.5.2.5 Examples 96 2.5.2.6 Iterative Importance Sampling 106 2.5.3 Summary 108 2.6 Summary 109 2.7 Problems 110 3 General Gaussian Detection 125 3.1 Detection of Gaussian Random Vectors 126 3.1.1 Real Gaussian Random Vectors 126 3.1.2 Circular Complex Gaussian Random Vectors 127 3.1.3 General Gaussian Detection 132 3.1.3.1 Real Gaussian Vectors 132 3.1.3.2 Circular Complex Gaussian Vectors 136 3.1.3.3 Summary 137 3.2 Equal Covariance Matrices 138 3.2.1 Independent Components with Equal Variance 142 3.2.2 Independent Components with Unequal Variances 146 3.2.3 General Case: Eigendecomposition 147 3.2.4 Optimum Signal Design 156 3.2.5 Interference Matrix: Estimator Subtractor 160 3.2.6 Low-Rank Models 165 3.2.7 Summary 173 3.3 Equal Mean Vectors 174 3.3.1 Diagonal Covariance Matrix on H0: Equal Variance 175 3.3.1.1 Independent, Identically Distributed Signal Components 177 3.3.1.2 Independent Signal Components: Unequal Variances 178 3.3.1.3 Correlated Signal Components 179 3.3.1.4 Low-Rank Signal Model 184 3.3.1.5 Symmetric Hypotheses, Uncorrelated Noise 186 3.3.2 Nondiagonal Covariance Matrix on H0 191 3.3.2.1 Signal on H1 Only 191 3.3.2.2 Signal on Both Hypotheses 195 3.3.3 Summary 196 3.4 General Gaussian 197 3.4.1 Real Gaussian Model 197 3.4.2 Circular Complex Gaussian Model 198 3.4.3 Single Quadratic Form 201 3.4.4 Summary 208 3.5 M Hypotheses 209 3.6 Summary 213 3.7 Problems 215 4 Classical Parameter Estimation 230 4.1 Introduction 230 4.2 Scalar Parameter Estimation 232 4.2.1 Random Parameters: Bayes Estimation 232 4.2.2 Nonrandom Parameter Estimation 246 4.2.3 Bayesian Bounds 261 4.2.3.1 Lower Bound on the MSE 261 4.2.3.2 Asymptotic Behavior 265 4.2.4 Case Study 268 4.2.5 Exponential Family 279 4.2.5.1 Nonrandom Parameters 279 4.2.5.2 Random Parameters 287 4.2.6 Summary of Scalar Parameter Estimation 292 4.3 Multiple Parameter Estimation 293 4.3.1 Estimation Procedures 293 4.3.1.1 Random Parameters 293 4.3.1.2 Nonrandom Parameters 296 4.3.2 Measures of Error 296 4.3.2.1 Nonrandom Parameters 296 4.3.2.2 Random Parameters 299 4.3.3 Bounds on Estimation Error 299 4.3.3.1 Nonrandom Parameters 299 4.3.3.2 Random Parameters 316 4.3.4 Exponential Family 321 4.3.4.1 Nonrandom Parameters 321 4.3.4.2 Random Parameters 324 4.3.5 Nuisance Parameters 325 4.3.5.1 Nonrandom Parameters 325 4.3.5.2 Random Parameters 326 4.3.5.3 Hybrid Parameters 328 4.3.6 Hybrid Parameters 328 4.3.6.1 Joint ML and MAP Estimation 329 4.3.6.2 Nuisance Parameters 331 4.3.7 Summary of Multiple Parameter Estimation 331 4.4 Global Bayesian Bounds 332 4.4.1 Covariance Inequality Bounds 333 4.4.1.1 Covariance Inequality 333 4.4.1.2 Bayesian Bounds 334 4.4.1.3 Scalar Parameters 334 4.4.1.4 Vector Parameters 340 4.4.1.5 Combined Bayesian Bounds 341 4.4.1.6 Functions of the Parameter Vector 342 4.4.1.7 Summary of Covariance Inequality Bounds 344 4.4.2 Method of Interval Estimation 345 4.4.3 Summary of Global Bayesian Bounds 348 4.5 Composite Hy