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Informationen zum Autor 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. Klappentext Non-Gaussian Statistical Communication Theory Since its inception in the late 1930s, Statistical Communication Theory (SCT) has grown into a major field of study, applicable to many branches of science. This authoritative and provocative text is a legacy left behind by the late Dr. David Middleton-a pioneer of SCT. He works from a vision?of communication as the central operation of discovery in all the sciences. His application of Non-Gaussian Space-Time methodology to SCT clarifies many unresolved signal and noise problems, especially those?prevalent in sonar and radar signal processing.?These and other SCT problems are?approached as cases of a larger space-time?signal processing problem. In addition to a review of the traditional theory, readers will find fascinating chapters on: Reception as a Statistical Decision Problem Space-Time Processing and Space-Time Matched Filters Wave-Number Frequency Analysis Optimum Detection with Scattering, Arrays, and Beam Forming Multiple Alternative Detection Bayes Extraction Systems Joint Detection and Estimation and Estimation When Signals May Not Be Present Canonical Channels Non-Gaussian Detection and Estimation Non-Gaussian and Inhomogeneous Wiener-Khintchine Problems At his death in 2008, Dr. Middleton had completed 10 of 24 planned chapters. Nine of these have been exhaustively edited by Series Editor John Anderson and are presented here, together with Middleton's original plan for the entire book. Forewords by Middleton's colleague Vincent Poor and by the Editor show how this massive project was brought to completion. Zusammenfassung 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. Inhaltsverzeichnis 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 Arr...
List of contents
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 Me
