SIMON HAYKIN, PhD, is Distinguished University Professor in the Department of Electrical and Computer Engineering at McMaster University. He has pioneered signal-processing techniques and systems for radar and communication applications, and authored several acclaimed textbooks. Dr. Haykin has received numerous awards for his research including Honorary Doctor of Technical Sciences from ETH Zurich, Switzerland, and the first International Union of Radio Science Henry Booker Gold Medal.

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• Preface xiContributors List xiii1. Introduction 1Simon HaykinExperimental Radar Facilities 2Organization of the Book 5Part I Radar Spectral Analysis2. Angle-of-Arrival Estimation in the Presence of Multipath 11Anastasios Drosopoulos and Simon Haykin2.1 Introduction 112.2 The Low-Angle Tracking Radar Problem 132.3 Spectrum Estimation Background 142.3.1 The Fundamental Equation of Spectrum Estimation 172.4 Thomson’s Multi-Taper Method 182.4.1 Prolate Spheroidal Wavefunctions and Sequences 192.5 Test Dataset and a Comparison of Some Popular Spectrum Estimation Procedures 232.5.1 Classical Spectrum Estimation 262.5.2 MUSIC and MFBLP 272.6 Multi-taper Spectrum Estimation 282.6.1 The Adaptive Spectrum 282.6.2 The Composite Spectrum 322.6.3 Computing the Crude, Adaptive, and Composite Spectra 332.7 F-Test for the Line Components 352.7.1 Brief Outline of the F-Test 352.7.2 The Point Regression Single-Line F-Test 372.7.3 The Integral Regression Single-Line F-Test 392.7.4 The Point Regression Double-Line F-Test 422.7.5 The Integral Regression Double-Line F-Test 462.7.6 Line Component Extraction 472.7.7 Prewhitening 542.7.8 Multiple Snapshots 572.7.9 Multiple Snapshot, Single-Line, Point-Regression F-Tests 572.7.10 Multiple-Snapshot, Double-Line Point-Regression F-Tests 592.8 Experimental Data Description for a Low-Angle Tracking Radar Study 602.9 Angle-of-Arrival (AOA) Estimation 632.10 Diffuse Multipath Spectrum Estimation 782.11 Discussion 85References 883. Time–Frequency Analysis of Sea Clutter 91David J. Thomson and Simon Haykin3.1 Introduction 913.2 An Overview of Nonstationary Behavior and Time–Frequency Analysis 923.3 Theoretical Background on Nonstationarity 943.3.1 Multi-taper Estimates 973.3.2 Spectrum Estimation as an Inverse Problem 983.4 High-Resolution Multi-taper Spectrograms 993.4.1 Nonstationary Quadratic-Inverse Theory 1013.4.2 Multi-taper Estimates of the Loève Spectrum 1033.5 Spectrum Analysis of Radar Signals 1043.6 Discussion 1113.6.1 Target Detection Rooted in Learning 112References 113Part II Dynamic Models4. Dynamics of Sea Clutter 119Simon Haykin, Rembrandt Bakker, and Brian Currie4.1 Introduction 1194.2 Statistical Nature of Sea Clutter: Classical Approach 1234.2.1 Background 1234.2.2 Current Models 1264.3 Is There a Radar Clutter Attractor? 1304.3.1 Nonlinear Dynamics 1304.3.2 Chaotic Invariants 1324.3.3 Inconclusive Experimental Results on the Chaotic Invariants of Sea Clutter 1334.3.4 Dynamic Reconstruction 1344.3.5 Chaos, a Self-Fulfilling Prophecy? 1374.4 Hybrid AM/FM Model of Sea Clutter 1394.4.1 Radar Return Plots 1394.4.2 Rayleigh Fading 1394.4.3 Time-Doppler Spectra 1424.4.4 Evidence for Amplitude Modulation, Frequency Modulation, and More 1444.4.5 Modeling Sea Clutter as a Nonstationary Complex Autoregressive Process 1464.5 Discussion 1504.5.1 Nonlinear Dynamics of Sea Clutter 1504.5.2 Autoregressive Modeling of Sea Clutter 1504.5.3 State-Space Theory 1514.5.4 Nonlinear Dynamical Approach Versus Classical Statistical Approach 1524.5.5 Stochastic Chaos 153References 155Appendix A Specifications of the Three Sea-Clutter Sets Used in This  157Chapter 5. Sea-Clutter Nonstationarity: The Influence of Long Waves 159Maria Greco and Fulvio Gini5.1 Introduction 1595.2 Radar and Data Description 1635.3 Statistical Data Analyses 1645.4 Modulation of Long Waves: Hybrid AM/FM Model 1695.5 Nonstationary AR Model 1795.6 Parametric Analysis of Texture Process 1815.7 Discussion 1885.7.1 Autoregressive Modeling of Sea Clutter 1895.7.2 Cyclostationarity of Sea Clutter 189References 1896. Two New Strategies for Target Detection in Sea Clutter 193Rembrandt Bakker, Brian Currie, and Simon Haykin6.1 Introduction 1936.2 Bayesian Direct Filtering Procedure 1956.2.1 Single-Target Scenario 1956.2.2 Conditioning on Past and Future Measurements 1966.3 Operational Details 1976.3.1 Experimental Data 1976.3.2 Statistics of Sea Clutter 1976.3.3 Statistics of Target Returns 1996.3.4 Motion Model of the Target 2006.4 Experimental Results on the Bayesian Direct Filter 2006.5 Additional Notes on the Bayesian Direct Filter 2046.6 Correlation Anomally Detection Strategy 2056.7 Experimental Comparison of the Bayesian Direct Filter and Correlation Anomaly Receiver 2066.7.1 Target-to-Interference Ratio 2076.7.2 Receiver Comparison 2076.8 Discussion 2176.8.1 Further Research 218References 219Index 221

SIMON HAYKIN, PhD, is Distinguished University Professor in the Department of Electrical and Computer Engineering at McMaster University. He has pioneered signal-processing techniques and systems for radar and communication applications, and authored several acclaimed textbooks. Dr. Haykin has received numerous awards for his research including Honorary Doctor of Technical Sciences from ETH Zurich, Switzerland, and the first International Union of Radio Science Henry Booker Gold Medal.

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• Preface xiContributors List xiii1. Introduction 1Simon HaykinExperimental Radar Facilities 2Organization of the Book 5Part I Radar Spectral Analysis2. Angle-of-Arrival Estimation in the Presence of Multipath 11Anastasios Drosopoulos and Simon Haykin2.1 Introduction 112.2 The Low-Angle Tracking Radar Problem 132.3 Spectrum Estimation Background 142.3.1 The Fundamental Equation of Spectrum Estimation 172.4 Thomson’s Multi-Taper Method 182.4.1 Prolate Spheroidal Wavefunctions and Sequences 192.5 Test Dataset and a Comparison of Some Popular Spectrum Estimation Procedures 232.5.1 Classical Spectrum Estimation 262.5.2 MUSIC and MFBLP 272.6 Multi-taper Spectrum Estimation 282.6.1 The Adaptive Spectrum 282.6.2 The Composite Spectrum 322.6.3 Computing the Crude, Adaptive, and Composite Spectra 332.7 F-Test for the Line Components 352.7.1 Brief Outline of the F-Test 352.7.2 The Point Regression Single-Line F-Test 372.7.3 The Integral Regression Single-Line F-Test 392.7.4 The Point Regression Double-Line F-Test 422.7.5 The Integral Regression Double-Line F-Test 462.7.6 Line Component Extraction 472.7.7 Prewhitening 542.7.8 Multiple Snapshots 572.7.9 Multiple Snapshot, Single-Line, Point-Regression F-Tests 572.7.10 Multiple-Snapshot, Double-Line Point-Regression F-Tests 592.8 Experimental Data Description for a Low-Angle Tracking Radar Study 602.9 Angle-of-Arrival (AOA) Estimation 632.10 Diffuse Multipath Spectrum Estimation 782.11 Discussion 85References 883. Time–Frequency Analysis of Sea Clutter 91David J. Thomson and Simon Haykin3.1 Introduction 913.2 An Overview of Nonstationary Behavior and Time–Frequency Analysis 923.3 Theoretical Background on Nonstationarity 943.3.1 Multi-taper Estimates 973.3.2 Spectrum Estimation as an Inverse Problem 983.4 High-Resolution Multi-taper Spectrograms 993.4.1 Nonstationary Quadratic-Inverse Theory 1013.4.2 Multi-taper Estimates of the Loève Spectrum 1033.5 Spectrum Analysis of Radar Signals 1043.6 Discussion 1113.6.1 Target Detection Rooted in Learning 112References 113Part II Dynamic Models4. Dynamics of Sea Clutter 119Simon Haykin, Rembrandt Bakker, and Brian Currie4.1 Introduction 1194.2 Statistical Nature of Sea Clutter: Classical Approach 1234.2.1 Background 1234.2.2 Current Models 1264.3 Is There a Radar Clutter Attractor? 1304.3.1 Nonlinear Dynamics 1304.3.2 Chaotic Invariants 1324.3.3 Inconclusive Experimental Results on the Chaotic Invariants of Sea Clutter 1334.3.4 Dynamic Reconstruction 1344.3.5 Chaos, a Self-Fulfilling Prophecy? 1374.4 Hybrid AM/FM Model of Sea Clutter 1394.4.1 Radar Return Plots 1394.4.2 Rayleigh Fading 1394.4.3 Time-Doppler Spectra 1424.4.4 Evidence for Amplitude Modulation, Frequency Modulation, and More 1444.4.5 Modeling Sea Clutter as a Nonstationary Complex Autoregressive Process 1464.5 Discussion 1504.5.1 Nonlinear Dynamics of Sea Clutter 1504.5.2 Autoregressive Modeling of Sea Clutter 1504.5.3 State-Space Theory 1514.5.4 Nonlinear Dynamical Approach Versus Classical Statistical Approach 1524.5.5 Stochastic Chaos 153References 155Appendix A Specifications of the Three Sea-Clutter Sets Used in This  157Chapter 5. Sea-Clutter Nonstationarity: The Influence of Long Waves 159Maria Greco and Fulvio Gini5.1 Introduction 1595.2 Radar and Data Description 1635.3 Statistical Data Analyses 1645.4 Modulation of Long Waves: Hybrid AM/FM Model 1695.5 Nonstationary AR Model 1795.6 Parametric Analysis of Texture Process 1815.7 Discussion 1885.7.1 Autoregressive Modeling of Sea Clutter 1895.7.2 Cyclostationarity of Sea Clutter 189References 1896. Two New Strategies for Target Detection in Sea Clutter 193Rembrandt Bakker, Brian Currie, and Simon Haykin6.1 Introduction 1936.2 Bayesian Direct Filtering Procedure 1956.2.1 Single-Target Scenario 1956.2.2 Conditioning on Past and Future Measurements 1966.3 Operational Details 1976.3.1 Experimental Data 1976.3.2 Statistics of Sea Clutter 1976.3.3 Statistics of Target Returns 1996.3.4 Motion Model of the Target 2006.4 Experimental Results on the Bayesian Direct Filter 2006.5 Additional Notes on the Bayesian Direct Filter 2046.6 Correlation Anomally Detection Strategy 2056.7 Experimental Comparison of the Bayesian Direct Filter and Correlation Anomaly Receiver 2066.7.1 Target-to-Interference Ratio 2076.7.2 Receiver Comparison 2076.8 Discussion 2176.8.1 Further Research 218References 219Index 221

SIMON HAYKIN, PhD, is Distinguished University Professor in the Department of Electrical and Computer Engineering at McMaster University. He has pioneered signal-processing techniques and systems for radar and communication applications, and authored several acclaimed textbooks. Dr. Haykin has received numerous awards for his research including Honorary Doctor of Technical Sciences from ETH Zurich, Switzerland, and the first International Union of Radio Science Henry Booker Gold Medal.

Select Guide Rating

• Preface xiContributors List xiii1. Introduction 1Simon HaykinExperimental Radar Facilities 2Organization of the Book 5Part I Radar Spectral Analysis2. Angle-of-Arrival Estimation in the Presence of Multipath 11Anastasios Drosopoulos and Simon Haykin2.1 Introduction 112.2 The Low-Angle Tracking Radar Problem 132.3 Spectrum Estimation Background 142.3.1 The Fundamental Equation of Spectrum Estimation 172.4 Thomson’s Multi-Taper Method 182.4.1 Prolate Spheroidal Wavefunctions and Sequences 192.5 Test Dataset and a Comparison of Some Popular Spectrum Estimation Procedures 232.5.1 Classical Spectrum Estimation 262.5.2 MUSIC and MFBLP 272.6 Multi-taper Spectrum Estimation 282.6.1 The Adaptive Spectrum 282.6.2 The Composite Spectrum 322.6.3 Computing the Crude, Adaptive, and Composite Spectra 332.7 F-Test for the Line Components 352.7.1 Brief Outline of the F-Test 352.7.2 The Point Regression Single-Line F-Test 372.7.3 The Integral Regression Single-Line F-Test 392.7.4 The Point Regression Double-Line F-Test 422.7.5 The Integral Regression Double-Line F-Test 462.7.6 Line Component Extraction 472.7.7 Prewhitening 542.7.8 Multiple Snapshots 572.7.9 Multiple Snapshot, Single-Line, Point-Regression F-Tests 572.7.10 Multiple-Snapshot, Double-Line Point-Regression F-Tests 592.8 Experimental Data Description for a Low-Angle Tracking Radar Study 602.9 Angle-of-Arrival (AOA) Estimation 632.10 Diffuse Multipath Spectrum Estimation 782.11 Discussion 85References 883. Time–Frequency Analysis of Sea Clutter 91David J. Thomson and Simon Haykin3.1 Introduction 913.2 An Overview of Nonstationary Behavior and Time–Frequency Analysis 923.3 Theoretical Background on Nonstationarity 943.3.1 Multi-taper Estimates 973.3.2 Spectrum Estimation as an Inverse Problem 983.4 High-Resolution Multi-taper Spectrograms 993.4.1 Nonstationary Quadratic-Inverse Theory 1013.4.2 Multi-taper Estimates of the Loève Spectrum 1033.5 Spectrum Analysis of Radar Signals 1043.6 Discussion 1113.6.1 Target Detection Rooted in Learning 112References 113Part II Dynamic Models4. Dynamics of Sea Clutter 119Simon Haykin, Rembrandt Bakker, and Brian Currie4.1 Introduction 1194.2 Statistical Nature of Sea Clutter: Classical Approach 1234.2.1 Background 1234.2.2 Current Models 1264.3 Is There a Radar Clutter Attractor? 1304.3.1 Nonlinear Dynamics 1304.3.2 Chaotic Invariants 1324.3.3 Inconclusive Experimental Results on the Chaotic Invariants of Sea Clutter 1334.3.4 Dynamic Reconstruction 1344.3.5 Chaos, a Self-Fulfilling Prophecy? 1374.4 Hybrid AM/FM Model of Sea Clutter 1394.4.1 Radar Return Plots 1394.4.2 Rayleigh Fading 1394.4.3 Time-Doppler Spectra 1424.4.4 Evidence for Amplitude Modulation, Frequency Modulation, and More 1444.4.5 Modeling Sea Clutter as a Nonstationary Complex Autoregressive Process 1464.5 Discussion 1504.5.1 Nonlinear Dynamics of Sea Clutter 1504.5.2 Autoregressive Modeling of Sea Clutter 1504.5.3 State-Space Theory 1514.5.4 Nonlinear Dynamical Approach Versus Classical Statistical Approach 1524.5.5 Stochastic Chaos 153References 155Appendix A Specifications of the Three Sea-Clutter Sets Used in This  157Chapter 5. Sea-Clutter Nonstationarity: The Influence of Long Waves 159Maria Greco and Fulvio Gini5.1 Introduction 1595.2 Radar and Data Description 1635.3 Statistical Data Analyses 1645.4 Modulation of Long Waves: Hybrid AM/FM Model 1695.5 Nonstationary AR Model 1795.6 Parametric Analysis of Texture Process 1815.7 Discussion 1885.7.1 Autoregressive Modeling of Sea Clutter 1895.7.2 Cyclostationarity of Sea Clutter 189References 1896. Two New Strategies for Target Detection in Sea Clutter 193Rembrandt Bakker, Brian Currie, and Simon Haykin6.1 Introduction 1936.2 Bayesian Direct Filtering Procedure 1956.2.1 Single-Target Scenario 1956.2.2 Conditioning on Past and Future Measurements 1966.3 Operational Details 1976.3.1 Experimental Data 1976.3.2 Statistics of Sea Clutter 1976.3.3 Statistics of Target Returns 1996.3.4 Motion Model of the Target 2006.4 Experimental Results on the Bayesian Direct Filter 2006.5 Additional Notes on the Bayesian Direct Filter 2046.6 Correlation Anomally Detection Strategy 2056.7 Experimental Comparison of the Bayesian Direct Filter and Correlation Anomaly Receiver 2066.7.1 Target-to-Interference Ratio 2076.7.2 Receiver Comparison 2076.8 Discussion 2176.8.1 Further Research 218References 219Index 221