David Middleton

Non-Gaussian Statistical Communication Theory (eBook, ePUB)

• Format: ePub

Produktbeschreibung

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 href="mailto:[e-mail]">[e-mail] to request Ch.10

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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 664
• Erscheinungstermin: 11. Mai 2012
• Englisch
• ISBN-13: 9781118161951
• Artikelnr.: 37351168

Autorenporträt

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