Foreword |
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xv | |
Visualizing the Invisible |
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xvii | |
Acknowledgments |
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xxi | |
About the Author |
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xxiii | |
Editor's Note |
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xxv | |
Introduction |
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1 | (14) |
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1 Reception as a Statistical Decision Problem |
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15 | (62) |
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1.1 Signal Detection and Estimation |
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15 | (2) |
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1.2 Signal Detection and Estimation |
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17 | (5) |
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17 | (3) |
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1.2.2 Types of Extraction |
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20 | (1) |
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1.2.3 Other Reception Problems |
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21 | (1) |
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1.3 The Reception Situation in General Terms |
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22 | (5) |
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1.3.1 Assumptions: Space-Time Sampling |
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22 | (3) |
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25 | (1) |
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1.3.3 The Decision Problem |
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26 | (1) |
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1.3.4 The Generic Similarity of Detection and Extraction |
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27 | (1) |
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27 | (8) |
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1.4.1 Evaluation Functions |
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27 | (3) |
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1.4.2 System Comparisons and Error Probabilities |
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30 | (1) |
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1.4.3 Optimization: Bayes Systems |
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31 | (1) |
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1.4.4 Optimization: Minimax Systems |
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32 | (3) |
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1.5 A Summary of Basic Definitions and Principal Theorems |
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35 | (5) |
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1.5.1 Some General Properties of Optimum Decision Rules |
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35 | (1) |
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36 | (1) |
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37 | (1) |
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1.5.4 Remarks: Prior Probabilities, Cost Assignments, and System Invariants |
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38 | (2) |
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1.6 Preliminaries: Binary Bayes Detection |
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40 | (6) |
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1.6.1 Formulation I: Binary On-Off Signal Detection |
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42 | (1) |
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43 | (1) |
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43 | (2) |
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1.6.4 Error Probabilities |
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45 | (1) |
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1.7 Optimum Detection: On-Off Optimum Processing Algorithms |
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46 | (4) |
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1.7.1 The Logarithmic GLRT |
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48 | (1) |
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1.7.2 Remarks on the Bayes Optimality of the GLR |
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48 | (2) |
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1.8 Special On-Off Optimum Binary Systems |
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50 | (7) |
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1.8.1 Neyman-Pearson Detection Theory |
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50 | (1) |
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1.8.2 The Ideal Observer Detection System |
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51 | (1) |
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52 | (1) |
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1.8.4 Maximum Aposteriori (MAP) Detectors from a Bayesian Viewpoint |
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53 | (4) |
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1.8.5 Bayesian Sequential Detectors |
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57 | (1) |
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1.9 Optimum Detection: On-Off Performance Measures and System Comparisons |
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57 | (12) |
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1.9.1 Error Probabilities: Optimum Systems |
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58 | (7) |
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1.9.2 Error Probabilities: Suboptimum Systems |
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65 | (1) |
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1.9.3 Decision Curves and System Comparisons |
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66 | (3) |
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1.10 Binary Two-Signal Detection: Disjoint and Overlapping Hypothesis Classes |
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69 | (4) |
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1.10.1 Disjoint Signal Classes |
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69 | (1) |
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1.10.2 Overlapping Hypothesis Classes |
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70 | (3) |
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73 | (4) |
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74 | (3) |
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2 Space-Time Covariances and Wave Number Frequency Spectra: I. Noise and Signals with Continuous and Discrete Sampling |
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77 | (64) |
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2.1 Inhomogeneous and Nonstationary Signal and Noise Fields I: Waveforms, Beam Theory, Covariances, and Intensity Spectra |
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78 | (13) |
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2.1.1 Signal Normalization |
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79 | (1) |
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2.1.2 Inhomogeneous Nonstationary (Non-WS-HS) Noise Covariances |
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80 | (3) |
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83 | (5) |
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2.1.4 Noise and Signal Field Covariances: Narrowband Cases |
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88 | (3) |
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2.2 Continuous Space-Time Wiener-Khintchine Relations |
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91 | (11) |
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2.2.1 Directly Sampled Approximation of the W-Kh Relations (Hom-Stat Examples) |
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93 | (2) |
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2.2.2 Extended Wiener-Khintchine Theorems: Continuous Inhomogeneous and Nonstationary Random (Scalar) Fields |
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95 | (5) |
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2.2.3 The Important Special Case of Homogeneous---Stationary Fields---Finite and Infinite Samples |
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100 | (2) |
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2.3 The W-Kh Relations for Discrete Samples in the Non-Hom-Stat Situation |
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102 | (6) |
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2.3.1 The Amplitude Spectrum for Discrete Samples |
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102 | (5) |
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107 | (1) |
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2.4 The Wiener-Khintchine Relations for Discretely Sampled Random Fields |
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108 | (7) |
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2.4.1 Discrete Hom-Stat Wiener-Khintchine Theorem: Periodic Sampling and Finite and Infinite Samples |
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110 | (2) |
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112 | (3) |
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2.5 Aperture and Arrays---I: An Introduction |
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115 | (23) |
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2.5.1 Transmission: Apertures and Their Fourier Equivalents |
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116 | (4) |
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2.5.2 Transmission: The Propagating Field and Its Source Function |
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120 | (6) |
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2.5.3 Point Arrays: Discrete Spatial Sampling |
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126 | (3) |
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129 | (5) |
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2.5.5 Narrowband Signals and Fields |
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134 | (3) |
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2.5.6 Some General Observations |
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137 | (1) |
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138 | (3) |
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139 | (2) |
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3 Optimum Detection, Space-Time Matched Filters, and Beam Forming in Gaussian Noise Fields |
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141 | (98) |
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3.1 Optimum Detection I: Selected Gaussian Prototypes---Coherent Reception |
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142 | (12) |
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3.1.1 Optimum Coherent Detection. Completely Known Deterministic Signals in Gauss Noise |
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142 | (4) |
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146 | (4) |
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3.1.3 Array Processing II: Beam Forming with Linear Arrays |
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150 | (4) |
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3.2 Optimum Detection II: Selected Gaussian Prototypes---Incoherent Reception |
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154 | (22) |
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3.2.1 Incoherent Detection: I. Narrowband Deterministic Signals |
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154 | (15) |
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3.2.2 Incoherent Detection II. Deterministic Narrowband Signals with Slow Rayleigh Fading |
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169 | (3) |
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3.2.3 Incoherent Detection III: Narrowband Equivalent Envelope Inputs---Representations |
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172 | (4) |
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3.3 Optimal Detection III: Slowly Fluctuating Noise Backgrounds |
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176 | (12) |
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176 | (4) |
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3.3.2 Narrowband Incoherent Detection Algorithms |
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180 | (3) |
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3.3.3 Incoherent Detection of Broadband Signals in Normal Noise |
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183 | (5) |
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3.4 Bayes Matched Filters and Their Associated Bilinear and Quadratic Forms, I |
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188 | (31) |
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3.4.1 Coherent Reception: Causal Matched Filters (Type 1) |
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190 | (2) |
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3.4.2 Incoherent Reception: Causal Matched Filters (Type 1) |
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192 | (3) |
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3.4.3 Incoherent Reception-Realizable Matched Filters; Type 2 |
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195 | (3) |
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3.4.4 Wiener-Kolmogoroff Filters |
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198 | (2) |
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3.4.5 Extensions: Clutter, Reverberation, and Ambient Noise |
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200 | (2) |
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3.4.6 Matched Filters and Their Separation in Space and Time I |
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202 | (5) |
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3.4.7 Solutions of the Discrete Integral Equations |
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207 | (7) |
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214 | (1) |
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3.4.9 Signal-to-Noise Ratios, Processing Gains, and Minimum Detectable Signals. I |
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214 | (5) |
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3.5 Bayes Matched Filters in the Wave Number-Frequency Domain |
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219 | (16) |
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3.5.1 Fourier Transforms of Discrete Series |
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219 | (11) |
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3.5.2 Independent Beam Forming and Temporal Processing |
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230 | (5) |
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235 | (4) |
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235 | (4) |
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4 Multiple Alternative Detection |
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239 | (32) |
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4.1 Multiple-Alternative Detection: The Disjoint Cases |
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239 | (15) |
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240 | (2) |
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4.1.2 Minimization of the Average Risk |
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242 | (2) |
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4.1.3 Geometric Interpretation |
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244 | (1) |
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245 | (5) |
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4.1.5 Error Probabilities, Average Risk, and System Evaluation |
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250 | (3) |
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253 | (1) |
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4.2 Overlapping Hypothesis Classes |
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254 | (8) |
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255 | (2) |
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4.2.2 Minimization of the Average Risk for Overlapping Hypothesis Classes |
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257 | (2) |
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4.2.3 Simple (K + 1) - ary Detection |
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259 | (1) |
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4.2.4 Error Probabilities, Average and Bayes Risk, and System Evaluations |
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260 | (2) |
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4.3 Detection with Decisions Rejection: Nonoverlapping Signal Classes |
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262 | (9) |
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4.3.1 Optimum (K + 1) - ary Decisions with Rejection |
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264 | (1) |
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4.3.2 Optimum (K + 1) - ary Decision with Rejection |
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265 | (1) |
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4.3.3 A Simple Cost Assignment |
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266 | (1) |
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267 | (3) |
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270 | (1) |
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5 Bayes Extraction Systems: Signal Estimation and Analysis, p(H1) = 1 |
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271 | (36) |
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5.1 Decision Theory Formulation |
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272 | (15) |
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5.1.1 Nonrandomized Decision Rules and Average Risk |
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272 | (2) |
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5.1.2 Bayes Extraction With a Simple Cost Function |
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274 | (4) |
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5.1.3 Bayes Extraction With a Quadratic Cost Function |
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278 | (3) |
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281 | (2) |
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5.1.5 Other Cost Functions |
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283 | (4) |
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5.2 Coherent Estimation of Amplitude (Deterministic Signals and Normal Noise, p(H1) = 1) |
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287 | (7) |
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5.2.1 Coherent Estimation of Signal Amplitude Quadratic Cost Function |
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287 | (3) |
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5.2.2 Coherent Estimation of Signal Amplitude (Simple Cost Functions) |
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290 | (1) |
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5.2.3 Estimations by (Real) θ Filters |
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291 | (2) |
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5.2.4 Biased and Unbiased Estimates |
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293 | (1) |
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5.3 Incoherent Estimation of Signal Amplitude (Deterministic Signals and Normal Noise, p(H1) = 1) |
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294 | (6) |
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5.3.1 Quadratic Cost Function |
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294 | (4) |
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5.3.2 "Simple" Cost Functions SCF1 (Incoherent Estimation) |
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298 | (2) |
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5.4 Waveform Estimation (Random Fields) |
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300 | (4) |
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5.4.1 Normal Noise Signals in Normal Noise Fields (Quadratic Cost Function) |
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300 | (1) |
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5.4.2 Normal Noise Signals in Normal Noise Fields ("Simple" Cost Functions) |
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301 | (3) |
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304 | (3) |
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305 | (2) |
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6 Joint Detection and Estimation, p(H1) ≤ 1: I. Foundations |
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307 | (74) |
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6.1 Joint Detection and Estimation under Prior Uncertainty [ p(H1) ≤ 1]: Formulation |
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309 | (6) |
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6.1.1 Case 1: No Coupling |
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312 | (2) |
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314 | (1) |
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6.2 Optimal Estimation [ p(H1) ≤ 1]: No Coupling |
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315 | (11) |
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6.2.1 Quadratic Cost Function: MMSE and Bayes Risk |
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316 | (3) |
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6.2.2 Simple Cost Functions: UMLE and Bayes Risk |
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319 | (7) |
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6.3 Simultaneous Joint Detection and Estimation: General Theory |
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326 | (24) |
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6.3.1 The General Case: Strong Coupling |
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326 | (5) |
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6.3.2 Special Cases I: Bayes Detection and Estimation With Weak Coupling |
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331 | (2) |
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6.3.3 Special Cases II: Further Discussion of γp<1|QCF for Weak or No Coupling |
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333 | (3) |
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6.3.4 Estimator Bias (p ≤ 1) |
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336 | (2) |
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6.3.5 Remarks on Interval Estimation, p(H1) ≤ 1 |
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338 | (1) |
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6.3.6 Detection Probabilities |
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339 | (2) |
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6.3.7 Waveform Estimation (p ≤ 1): Coupled and Uncoupled D and E |
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341 | (1) |
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6.3.8 Extensions and Modifications |
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342 | (3) |
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345 | (5) |
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6.4 Joint D and E: Examples-Estimation of Signal Amplitudes [ p(H1) ≤ 1] |
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350 | (28) |
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6.4.1 Amplitude Estimation, p(H1) = 1 |
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352 | (3) |
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6.4.2 Bayes Estimators and Bayes Error, p(H1) ≤ 1 |
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355 | (3) |
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6.4.3 Performance Degradation, p < 1 |
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358 | (9) |
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6.4.4 Acceptance or Rejection of the Estimator: Detection Probabilities |
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367 | (4) |
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6.4.5 Remarks on the Estimation of Signal Intensity l0 ≡ a20 |
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371 | (7) |
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6.5 Summary Remarks, p(H)1 ≤ 1: I---Foundations |
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378 | (3) |
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379 | (2) |
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7 Joint Detection and Estimation under Uncertainty, pk (H1) < 1. II. Multiple Hypotheses and Sequential Observations |
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381 | (54) |
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7.1 Jointly Optimum Detection and Estimation under Multiple Hypotheses, p(H1) ≤ 1 |
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382 | (18) |
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383 | (6) |
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7.1.2 Specific Cost Functions |
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389 | (7) |
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7.1.3 Special Cases: Binary Detection and Estimation |
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396 | (4) |
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7.2 Uncoupled Optimum Detection and Estimation, Multiple Hypotheses, and Overlapping Parameter Spaces |
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400 | (7) |
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7.2.1 A Generalized Cost Function for K-Signals with Overlapping Parameter Values |
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402 | (1) |
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7.2.2 QCF: Overlapping Hypothesis Classes |
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403 | (3) |
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7.2.3 Simple Cost Functions (SCF1,2): Joint D + E with Overlapping Hypotheses Classes |
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406 | (1) |
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7.3 Simultaneous Detection and Estimation: Sequences of Observations and Decisions |
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407 | (21) |
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7.3.1 Sequential Observations and Unsupervised Learning: I. Binary Systems with Joint Uncoupled D + E |
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407 | (7) |
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7.3.2 Sequential Observations and Unsupervised Learning: II. Joint D + E for Binary Systems with Strong and Weak Coupling |
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414 | (3) |
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7.3.3 Sequential Observations and Unsupervised Learning: III. Joint D + E Under Multiple Hypotheses with Strong and Weak Coupling and Overlapping Hypotheses Classes |
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417 | (6) |
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7.3.4 Sequential Observations and Overlapping Multiple Hypothesis Classes: Joint D + E with No Coupling |
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423 | (2) |
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7.3.5 Supervised Learning (Self-Taught Mode): An Introduction |
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425 | (3) |
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428 | (7) |
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432 | (3) |
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8 The Canonical Channel I: Scalar Field Propagation in a Deterministic Medium |
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435 | (104) |
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8.1 The Generic Deterministic Channel: Homogeneous Unbounded Media |
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437 | (28) |
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8.1.1 Components of the Generic Channel: Coupling |
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438 | (1) |
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8.1.2 Propagation in An Ideal Medium |
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439 | (2) |
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8.1.3 Green's Function for the Ideal Medium |
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441 | (7) |
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8.1.4 Causality, Regularity, and Reciprocity of the Green's Function |
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448 | (1) |
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8.1.5 Selected Green's Functions |
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449 | (4) |
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8.1.6 A Generalized Huygens Principle: Solution for the Homogeneous Field αH |
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453 | (10) |
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8.1.7 The Explicit Role of the Aperture or Array |
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463 | (2) |
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8.2 The Engineering Approach: I---The Medium and Channel as Time-Varying Linear Filters (Deterministic Media) |
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465 | (8) |
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8.2.1 Equivalent Temporal Filters |
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466 | (5) |
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8.2.2 Causality: Extensions of the Paley-Wiener Criterion |
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471 | (2) |
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8.3 Inhomogeneous Media and Channels---Deterministic Scatter and Operational Solutions |
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473 | (21) |
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8.3.1 Deterministic Volume and Surface Scatter: The Green's Function and Associated Field α(Q) |
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475 | (3) |
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8.3.2 The Associated Field and Equivalent Solutions for Volumes and Surfaces |
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478 | (2) |
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8.3.3 Inhomogeneous Reciprocity |
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480 | (4) |
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8.3.4 The GHP for Inhomogeneous Deterministic Media including Backscatter |
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484 | (9) |
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8.3.5 Generalizations and Remarks |
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493 | (1) |
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8.4 The Deterministic Scattered Field in Wave Number-Frequency Space: Innovations |
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494 | (5) |
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8.4.1 Transform Operator Solutions |
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496 | (2) |
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8.4.2 Commutation and Convolution |
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498 | (1) |
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8.5 Extensions and Innovations, Multimedia Interactions |
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499 | (10) |
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8.5.1 The η-Form: Multimedia Interactions |
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500 | (3) |
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8.5.2 The Feedback Operational Representation and Solution |
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503 | (4) |
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8.5.3 An Estimation Procedure for the Deterministic Mass Operators Q and η |
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507 | (1) |
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8.5.4 The Engineering Approach II: Inhomogeneous Deterministic Media |
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508 | (1) |
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8.6 Energy Considerations |
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509 | (26) |
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8.6.1 Outline of the Variation Method |
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510 | (2) |
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8.6.2 Preliminary Remarks |
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512 | (2) |
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8.6.3 Energy Density and Density Flux: Direct Models---A Brief Introduction |
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514 | (2) |
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8.6.4 Equal Nonviscous Elastic Media |
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516 | (6) |
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8.6.5 Energy Densities and Flux Densities in the Dissipative Media |
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522 | (5) |
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8.6.6 Extensions: Arrays and Finite Duration Sources and Summary Remarks |
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527 | (8) |
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8.7 Summary: Results and Conclusions |
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535 | (4) |
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536 | (3) |
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9 The Canonical Channel II: Scattering in Random Media; "Classical" Operator Solutions |
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539 | (60) |
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9.1 Random Media: Operational Solutions---First- and Second-Order Moments |
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541 | (24) |
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9.1.1 Operator Forms: Moment Solutions and Dyson's Equation |
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543 | (8) |
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9.1.2 Dyson's Equation in Statistically Homogeneous and Stationary Media |
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551 | (9) |
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9.1.3 Example: The Statistical Structure of the Mass Operator Q(d)1, with (Q) = 0 |
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560 | (4) |
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564 | (1) |
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9.2 Higher Order Moments Operational Solutions for The Langevin Equation |
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565 | (15) |
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9.2.1 The Second-Order Moments: Analysis of the Bethe-Salpeter Equation (BSE) |
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565 | (3) |
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9.2.2 The Structure of Q(d)12 |
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568 | (2) |
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9.2.3 Higher-Order Moment Solutions (m ≥ 3) and Related Topics |
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570 | (2) |
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9.2.4 Transport Equations |
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572 | (2) |
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574 | (1) |
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9.2.6 Very Strong Scatter: Saturation ||η|| ~ 1 |
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575 | (4) |
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579 | (1) |
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9.3 Equivalent Representations: Elementary Feynman Diagrams |
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580 | (18) |
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581 | (5) |
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9.3.2 Diagram Approximations |
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586 | (8) |
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9.3.3 A Characterization of the Random Channel: First- and Second-Order-Moments I |
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594 | (2) |
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9.3.4 Elementary Statistics of the Received Field |
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596 | (2) |
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598 | (1) |
References |
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599 | (2) |
Appendix A1 |
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601 | (16) |
Index |
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617 | |