The book is based on the observation that communication is the central operation of discovery in all the sciences. In its "active mode" we use it to "interrogate" the physical world, sending appropriate "signals" and receiving nature's "reply". In the "passive mode" we receive nature's signals directly. Since we never know a prioriwhat particular return signal will be forthcoming, we must necessarily adopt a probabilistic model of communication. This has developed over the approximately seventy years since it's beginning, into a Statistical Communication Theory (or SCT). Here it is the set or…mehr
The book is based on the observation that communication is the central operation of discovery in all the sciences. In its "active mode" we use it to "interrogate" the physical world, sending appropriate "signals" and receiving nature's "reply". In the "passive mode" we receive nature's signals directly. Since we never know a prioriwhat particular return signal will be forthcoming, we must necessarily adopt a probabilistic model of communication. This has developed over the approximately seventy years since it's beginning, into a Statistical Communication Theory (or SCT). Here it is the set or ensemble of possible results which is meaningful. From this ensemble we attempt to construct in the appropriate model format, based on our understanding of the observed physical data and on the associated statistical mechanism, analytically represented by suitable probability measures.
Since its inception in the late '30's of the last century, and in particular subsequent to World War II, SCT has grown into a major field of study. As we have noted above, SCT is applicable to all branches of science. The latter itself is inherently and ultimately probabilistic at all levels. Moreover, in the natural world there is always a random background "noise" as well as an inherent a priori uncertainty in the presentation of deterministic observations, i.e. those which are specifically obtained, a posteriori.
The purpose of the book is to introduce Non-Gaussian statistical communication theory and demonstrate how the theory improves probabilistic model. The book was originally planed to include 24 chapters as seen in the table of preface. Dr. Middleton completed first 10 chapters prior to his passing in 2008. Bibliography which represents remaining chapters are put together by the author's close colleagues; Drs. Vincent Poor, Leon Cohen and John Anderson.
email pressbooks@ieee.org to request Ch.10
Produktdetails
Produktdetails
IEEE Press Series on Digital & Mobile Communication
David Middleton, PhD, graduated from Harvard University where he began his career at the institution's Radio Research Laboratory--working on radar countermeasures as well as passive and active jamming during World War II--before teaching there. A recipient of numerous prizes and awards related to his work on communication theory, Dr. Middleton was a fellow of the IEEE, the American Physical Society, the Acoustical Society of America, and the American Association for the Advancement of Science.
Inhaltsangabe
Foreword xv Visualizing the Invisible xvii Acknowledgments xxi About the Author xxiii Editor's Note xxv Introduction 1 1 Reception as a Statistical Decision Problem 15 1.1 Signal Detection and Estimation 15 1.2 Signal Detection and Estimation 17 1.3 The Reception Situation in General Terms 22 1.4 System Evaluation 27 1.5 A Summary of Basic Definitions and Principal Theorems 35 1.6 Preliminaries: Binary Bayes Detection 40 1.7 Optimum Detection: On-Off Optimum Processing Algorithms 46 1.8 Special On-Off Optimum Binary Systems 50 1.9 Optimum Detection: On-Off Performance Measures and System Comparisons 57 1.10 Binary Two-Signal Detection: Disjoint and Overlapping Hypothesis Classes 69 2 Space-Time Covariances and Wave Number Frequency Spectra: I. Noise and Signals with Continuous and Discrete Sampling 77 2.1 Inhomogeneous and Nonstationary Signal and Noise Fields I: Waveforms Beam Theory Covariances and Intensity Spectra 78 2.2 Continuous Space-Time Wiener-Khintchine Relations 91 2.3 The W-Kh Relations for Discrete Samples in the Non-Hom-Stat Situation 102 2.4 The Wiener-Khintchine Relations for Discretely Sampled Random Fields 108 2.5 Aperture and Arrays-I: An Introduction 115 2.6 Concluding Remarks 138 3 Optimum Detection Space-Time Matched Filters and Beam Forming in Gaussian Noise Fields 141 3.1 Optimum Detection I: Selected Gaussian Prototypes-Coherent Reception 142 3.2 Optimum Detection II: Selected Gaussian Prototypes-Incoherent Reception 154 3.3 Optimal Detection III: Slowly Fluctuating Noise Backgrounds 176 3.4 Bayes Matched Filters and Their Associated Bilinear and Quadratic Forms I 188 3.5 Bayes Matched Filters in the Wave Number-Frequency Domain 219 3.6 Concluding Remarks 235 4 Multiple Alternative Detection 239 4.1 Multiple-Alternative Detection: The Disjoint Cases 239 4.2 Overlapping Hypothesis Classes 254 4.3 Detection with Decisions Rejection: Nonoverlapping Signal Classes 262 5 Bayes Extraction Systems: Signal Estimation and Analysis p(H1) = 1 271 5.1 Decision Theory Formulation 272 5.2 Coherent Estimation of Amplitude (Deterministic Signals and Normal Noise p(H1) = 1) 287 5.3 Incoherent Estimation of Signal Amplitude (Deterministic Signals and Normal Noise p(H1) = 1) 294 5.4 Waveform Estimation (Random Fields) 300 5.5 Summary Remarks 304 6 Joint Detection and Estimation p(H1) <= 1: I. Foundations 307 6.1 Joint Detection and Estimation under Prior Uncertainty [p(H1)<= 1]: Formulation 309 6.2 Optimal Estimation [ p(H1) <= 1]: No Coupling 315 6.3 Simultaneous Joint Detection and Estimation: General Theory 326 6.4 Joint D and E: Examples-Estimation of Signal Amplitudes [p(H1) <= 1] 350 6.5 Summary Remarks p(H)1 <= 1: I-Foundations 378 7 Joint Detection and Estimation under Uncertainty pk(H1) II. Multiple Hypotheses and Sequential Observations 381 7.1 Jointly Optimum Detection and Estimation under Multiple Hypotheses p(H1) <= 1 382 7.2 Uncoupled Optimum Detection and Estimation Multiple Hypotheses and Overlapping Parameter Spaces 400 7.3 Simultaneous Detection and Estimation: Sequences of Observations and Decisions 407 7.4 Concluding Remarks 428 8 The Canonical Channel I: Scalar Field Propagation in a Deterministic Medium 435 8.1 The Generic Deterministic Channel: Homogeneous Unbounded Media 437 8.2 The Engineering Approach: I-The Medium and Channel as Time-Varying Linear Filters (Deterministic Media) 465 8.3 Inhomogeneous Media and Channels-Deterministic Scatter and Operational Solutions 473 8.4 The Deterministic Scattered Field in Wave Number-Frequency Space: Innovations 494 8.5 Extensions and Innovations Multimedia Interactions 499 8.6 Energy Considerations 509 8.7 Summary: Results and Conclusions 535 9 The Canonical Channel II: Scattering in Random Media; "Classical" Operator Solutions 539 9.1 Random Media: Operational Solutions-First- and Second-Order Moments 541 9.2 Higher Order Moments Operational Solutions for The Langevin Equation 565 9.3 Equivalent Representations: Elementary Feynman Diagrams 580 9.4 Summary Remarks 598 References 599 Appendix A1 601 Index 617
Foreword xv Visualizing the Invisible xvii Acknowledgments xxi About the Author xxiii Editor's Note xxv Introduction 1 1 Reception as a Statistical Decision Problem 15 1.1 Signal Detection and Estimation 15 1.2 Signal Detection and Estimation 17 1.3 The Reception Situation in General Terms 22 1.4 System Evaluation 27 1.5 A Summary of Basic Definitions and Principal Theorems 35 1.6 Preliminaries: Binary Bayes Detection 40 1.7 Optimum Detection: On-Off Optimum Processing Algorithms 46 1.8 Special On-Off Optimum Binary Systems 50 1.9 Optimum Detection: On-Off Performance Measures and System Comparisons 57 1.10 Binary Two-Signal Detection: Disjoint and Overlapping Hypothesis Classes 69 2 Space-Time Covariances and Wave Number Frequency Spectra: I. Noise and Signals with Continuous and Discrete Sampling 77 2.1 Inhomogeneous and Nonstationary Signal and Noise Fields I: Waveforms Beam Theory Covariances and Intensity Spectra 78 2.2 Continuous Space-Time Wiener-Khintchine Relations 91 2.3 The W-Kh Relations for Discrete Samples in the Non-Hom-Stat Situation 102 2.4 The Wiener-Khintchine Relations for Discretely Sampled Random Fields 108 2.5 Aperture and Arrays-I: An Introduction 115 2.6 Concluding Remarks 138 3 Optimum Detection Space-Time Matched Filters and Beam Forming in Gaussian Noise Fields 141 3.1 Optimum Detection I: Selected Gaussian Prototypes-Coherent Reception 142 3.2 Optimum Detection II: Selected Gaussian Prototypes-Incoherent Reception 154 3.3 Optimal Detection III: Slowly Fluctuating Noise Backgrounds 176 3.4 Bayes Matched Filters and Their Associated Bilinear and Quadratic Forms I 188 3.5 Bayes Matched Filters in the Wave Number-Frequency Domain 219 3.6 Concluding Remarks 235 4 Multiple Alternative Detection 239 4.1 Multiple-Alternative Detection: The Disjoint Cases 239 4.2 Overlapping Hypothesis Classes 254 4.3 Detection with Decisions Rejection: Nonoverlapping Signal Classes 262 5 Bayes Extraction Systems: Signal Estimation and Analysis p(H1) = 1 271 5.1 Decision Theory Formulation 272 5.2 Coherent Estimation of Amplitude (Deterministic Signals and Normal Noise p(H1) = 1) 287 5.3 Incoherent Estimation of Signal Amplitude (Deterministic Signals and Normal Noise p(H1) = 1) 294 5.4 Waveform Estimation (Random Fields) 300 5.5 Summary Remarks 304 6 Joint Detection and Estimation p(H1) <= 1: I. Foundations 307 6.1 Joint Detection and Estimation under Prior Uncertainty [p(H1)<= 1]: Formulation 309 6.2 Optimal Estimation [ p(H1) <= 1]: No Coupling 315 6.3 Simultaneous Joint Detection and Estimation: General Theory 326 6.4 Joint D and E: Examples-Estimation of Signal Amplitudes [p(H1) <= 1] 350 6.5 Summary Remarks p(H)1 <= 1: I-Foundations 378 7 Joint Detection and Estimation under Uncertainty pk(H1) II. Multiple Hypotheses and Sequential Observations 381 7.1 Jointly Optimum Detection and Estimation under Multiple Hypotheses p(H1) <= 1 382 7.2 Uncoupled Optimum Detection and Estimation Multiple Hypotheses and Overlapping Parameter Spaces 400 7.3 Simultaneous Detection and Estimation: Sequences of Observations and Decisions 407 7.4 Concluding Remarks 428 8 The Canonical Channel I: Scalar Field Propagation in a Deterministic Medium 435 8.1 The Generic Deterministic Channel: Homogeneous Unbounded Media 437 8.2 The Engineering Approach: I-The Medium and Channel as Time-Varying Linear Filters (Deterministic Media) 465 8.3 Inhomogeneous Media and Channels-Deterministic Scatter and Operational Solutions 473 8.4 The Deterministic Scattered Field in Wave Number-Frequency Space: Innovations 494 8.5 Extensions and Innovations Multimedia Interactions 499 8.6 Energy Considerations 509 8.7 Summary: Results and Conclusions 535 9 The Canonical Channel II: Scattering in Random Media; "Classical" Operator Solutions 539 9.1 Random Media: Operational Solutions-First- and Second-Order Moments 541 9.2 Higher Order Moments Operational Solutions for The Langevin Equation 565 9.3 Equivalent Representations: Elementary Feynman Diagrams 580 9.4 Summary Remarks 598 References 599 Appendix A1 601 Index 617
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