Cognitive Radar: The Knowledge-aided Fully Adaptive Approach

Cognitive Radar: The Knowledge-Aided Fully Adaptive Approach......Page 2
Contents......Page 6
Preface......Page 10
1.1 Why “Cognitive” Radar?......Page 14
1.2 Functional Elements and Characteristics of a Cognitive Radar Architecture......Page 15
1.2.1 Adaptive Transmit Capability......Page 18
1.2.2 Knowledge-Aided Processing......Page 24
1.3 Book Organization......Page 31
References......Page 32
2.1 Introduction......Page 36
2.2 Jointly Optimizing the Transmit and Receive Functions Case I: Maximizing SINR......Page 37
Example 2.1 Multipath Interference......Page 43
2.3 Jointly Optimizing the Transmit and Receive Functions Case II: Maximizing Signal-to-Clutter......Page 48
Example 2.2 Sidelobe Target Suppression: “Sidelobe Nulling on Transmit”......Page 50
Example 2.3 Optimal Pulse Shape for Maximizing SCR......Page 52
Example 2.4 Optimum Space-Time MIMO Processing for Clutter Suppression in Airborne MTI Radar......Page 55
2.4 Optimum MIMO Target Identification......Page 63
Example 2.5 Two-Target Identification Example......Page 66
2.5 Constrained Optimum MIMO Radar......Page 70
Example 2.7 Prenulling on Transmit......Page 72
Example 2.8 Relaxed Projection Example......Page 75
Example 2.9 Nonlinear FM (NLFM) to Achieve Constant Modulus......Page 78
Example 2.10 Matched Subspace Example......Page 85
Appendix 2.A: Infinite Duration (Steady State) Case......Page 87
References......Page 88
3.1 Introduction......Page 90
3.2 Transmit-Independent Channel Estimation......Page 91
Example 3.1 Adaptive Multipath Interference Mitigation......Page 92
Example 3.2 MIMO Cohere-on-Target......Page 94
3.4 Transmit-Dependent Channel Estimation......Page 97
Example 3.3 STAP-on-Transmit (STAP-Tx) Example......Page 98
Example 3.4 DDMA MIMO STAP Clutter Mitigation Example for GMTI Radar......Page 102
3.5 Theoretical Performance Bounds of the DDMA MIMOSTAP Approach......Page 105
References......Page 111
4.1 The Need for KA Radar......Page 114
4.2 Introduction to KA Radar: Back to “Bayes-ics”......Page 119
4.2.1 Indirect KA Radar: Intelligent Training and Filter Selection......Page 122
Example 4.1 Intelligent Filter Selection: Matching the Adaptive DoFs(ADoFs) to the Available Training Data......Page 124
4.2.2 Direct KA Radar: Bayesian Filtering and Data Prewhitening......Page 128
Example 4.2 Using Past Observations as a Prior Knowledge Source......Page 132
4.3 Real-Time KA Radar: The DARPA KASSPER Project......Page 136
4.3.1 Solution: Look-Ahead Scheduling......Page 138
Example 4.3 Balancing Throughput in a KASSPER HPEC Architecture......Page 142
4.3.2 Examples of a KA Architectures Developed by the DARPA/AFRL KASSPER Project......Page 145
4.4 KA Radar Epilogue......Page 154
References......Page 155
5.1 Cognitive Radar: The Fully Adaptive Knowledge-Aided Approach......Page 159
Example 5.1: A Cognitive Radar Architecture......Page 161
5.1.1 Informal Operational Narrative for a GMTI Radar......Page 163
5.2 Areas for Future Research and Development......Page 165
References......Page 166
About the Author......Page 168
Index......Page 170