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Summary
Summary
For the first time in one book, this unique volume brings together major new developments in optimal and adaptive multi-input, multi-output (MIMO) radar and knowledge-aided (KA) processing. These breakthroughs yield an entirely new dynamic radar architecture that possesses unprecedented capabilities for adaptation in challenging real-world environments. This practical resource includes many illustrative examples that help the reader with a number of diverse applications, from optimizing detection of weak targets in complex interference backgrounds, to target identification. Although packed with cutting-edge material, this book is written in an accessible style consistent with the author's previously well-received Space-Time Adaptive Processing for Radar (Artech House, 2003). Book jacket.
Author Notes
J. R. Guerci earned his Ph.D. in system engineering at the Polytechnic University, New York.
Dr. Guerci is deputy director, special projects office for The Defense Advanced Research Projects Agency (DARPA). A Senior Member of the IEEE and a Member of the IEEE Radar Systems Panel.
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Table of Contents
| Preface | p. 9 |
| Chapter 1 Introduction | p. 13 |
| 1.1 Why "Cognitive" Radar? | p. 13 |
| 1.2 Functional Elements and Characteristics of a Cognitive Radar Architecture | p. 14 |
| 1.2.1 Adaptive Transmit Capability | p. 17 |
| 1.2.2 Knowledge-Aided Processing | p. 23 |
| 1.3 Book Organization | p. 30 |
| References | p. 31 |
| Chapter 2 Optimum Multi-Input Multioutput (MIMO) Radar | p. 35 |
| 2.1 Introduction | p. 35 |
| 2.2 Jointly Optimizing the Transmit and Receive Functions Case I: Maximizing SINR | p. 36 |
| Example 2.1 Multipath Interference | p. 42 |
| 2.3 Jointly Optimizing the Transmit and Receive Functions Case II: Maximizing Signal-to-Clutter | p. 47 |
| Example 2.2 Sidelobe Target Suppression: "Sidelobe Nulling on Transmit" | p. 49 |
| Example 2.3 Optimal Pulse Shape for Maximizing SCR | p. 51 |
| Example 2.4 Optimum Space-Time MIMO Processing for Clutter Suppression in Airborne MTI Radar | p. 54 |
| 2.4 Optimum MIMO Target Identification | p. 62 |
| Example 2.5 Two-Target Identification Example | p. 64 |
| Example 2.6 Multitarget Identification Example | p. 69 |
| 2.5 Constrained Optimum MIMO Radar | p. 69 |
| Example 2.7 Prenulling on Transmit | p. 71 |
| Example 2.8 Relaxed Projection Example | p. 74 |
| Example 2.9 Nonlinear FM (NLFM) to Achieve Constant Modulus | p. 77 |
| Example 2.10 Matched Subspace Example | p. 84 |
| Appendix 2.A Infinite Duration (Steady State) Case | p. 86 |
| References | p. 87 |
| Chapter 3 Adaptive Multi-Input Multioutput (MIMO) Radar | p. 89 |
| 3.1 Introduction | p. 89 |
| 3.2 Transmit-Independent Channel Estimation | p. 90 |
| Example 3.1 Adaptive Multipath Interference Mitigation | p. 91 |
| 3.3 Dynamic MIMO Calibration | p. 93 |
| Example 3.2 MIMO Cohere-on-Target | p. 93 |
| 3.4 Transmit-Dependent Channel Estimation | p. 96 |
| Example 3.3 STAP-on-Transmit (STAP-Tx) Example | p. 97 |
| Example 3.4 DDMA MIMO STAP Clutter Mitigation Example for GMTI Radar | p. 102 |
| 3.5 Theoretical Performance Bounds of the DDMA MIMO STAP Approach | p. 104 |
| References | p. 110 |
| Chapter 4 Introduction to Knowledge-Aided (KA) Adaptive Radar | p. 113 |
| 4.1 The Need for KA Radar | p. 113 |
| 4.2 Introduction to KA Radar: Back to "Bayes-ics" | p. 118 |
| 4.2.1 Indirect KA Radar: Intelligent Training and Filter Selection | p. 121 |
| Example 4.1 Intelligent Filter Selection: Matching the Adaptive DoFs (ADoFs) to the Available Training Data | p. 123 |
| 4.2.2 Direct KA Radar: Bayesian Filtering and Data Prewhitening | p. 127 |
| Example 4.2 Using Past Observations as a Prior Knowledge Source | p. 131 |
| 4.3 Real-Time KA Radar: The DARPA KASSPER Project | p. 135 |
| 4.3.1 Solution: Look-Ahead Scheduling | p. 137 |
| Example 4.3 Balancing Throughput in a KASSPER HPEC Architecture | p. 141 |
| 4.3.2 Examples of a KA Architectures Developed by the DARPA/AFRL KASSPER Project | p. 144 |
| 4.4 KA Radar Epilogue | p. 153 |
| References | p. 154 |
| Chapter 5 Putting It All Together | p. 159 |
| 5.1 Cognitive Radar: The Fully Adaptive Knowledge-Aided Approach | p. 159 |
| Example 5.1 A Cognitive Radar Architecture | p. 160 |
| 5.1.1 Informal Operational Narrative for a GMTI Radar | p. 162 |
| 5.2 Areas for Future Research and Development | p. 164 |
| References | p. 165 |
| About the Author | p. 167 |
| Index | p. 169 |
