| Preface |
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11 | (4) |
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15 | (22) |
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1.1 Why "Cognitive" Radar? |
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15 | (1) |
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1.2 Functional Elements and Characteristics of a Cognitive Radar Architecture |
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16 | (16) |
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1.2.1 Adaptive Transmit Capability |
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19 | (5) |
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1.2.2 Knowledge-Aided Processing |
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24 | (5) |
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1.2.3 Optimum Resource Allocation and Scheduling for Cognitive Radar |
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29 | (2) |
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1.2.3 Optimum Resource Allocation and Scheduling for Cognitive Radar |
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31 | (1) |
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32 | (1) |
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32 | (5) |
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2 Optimum Multi-Input Multioutput Radar |
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37 | (50) |
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37 | (1) |
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2.2 Jointly Optimizing the Transmit and Receive Functions Case I: Maximizing SINR |
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38 | (5) |
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Example 2.1 Multipath Interference |
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43 | (5) |
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2.3 Jointly Optimizing the Transmit and Receive Functions Case II: Maximizing Signal-to-Clutter |
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48 | (1) |
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Example 2.2 Sidelobe Target Suppression: Sidelobe Nulling on Transmit |
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49 | (2) |
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Example 2.3 Optimal Pulse Shape for Maximizing SCR |
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51 | (3) |
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Example 2.4 Optimum Space-Time MIMO Processing for Clutter Suppression in Airborne MTI Radar |
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54 | (6) |
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2.4 Optimum MIMO Target Identification |
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60 | (3) |
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Example 2.5 Two-Target Identification Example |
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63 | (1) |
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64 | (2) |
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Example 2.6 Multitarget Identification Example |
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66 | (1) |
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2.5 Constrained Optimum MIMO Radar |
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67 | (1) |
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Case I Linear Constraints |
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67 | (1) |
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Example 2.7 Prenulling on Transmit |
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68 | (2) |
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Case II Nonlinear Constraints |
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70 | (1) |
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Relaxed Projection Approach |
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70 | (1) |
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Example 2.8 Relaxed Projection Example |
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71 | (1) |
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Constant Modulus and the Method of Stationary Phase |
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72 | (2) |
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Example 2.9 Nonlinear FM (NLFM) to Achieve Constant Modulus |
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74 | (6) |
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Example 2.10 Matched Subspace Example |
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80 | (1) |
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2.6 Recent Advances in Constrained Optimum MIMO |
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80 | (2) |
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82 | (2) |
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Appendix 2.A Infinite Duration (Steady State) Case |
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84 | (3) |
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87 | (32) |
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87 | (1) |
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3.2 Transmit-Independent Channel Estimation |
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88 | (2) |
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Example 3.1 Adaptive Multipath Interference Mitigation |
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90 | (1) |
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3.3 Dynamic MIMO Calibration |
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91 | (1) |
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Example 3.2 MIMO Cohere-on-Target |
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91 | (2) |
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3.4 Transmit-Dependent Channel Estimation |
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93 | (1) |
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Example 3.2 STAP-Tx Example |
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94 | (3) |
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Example 3.2 DDMA MIMO STAP Clutter Mitigation Example for GMTI Radar |
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97 | (4) |
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3.5 Theoretical Performance Bounds of the DDMA MIMO STAP Approach |
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101 | (5) |
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3.6 Nonorthogonal MIMO Probing for Channel Estimation |
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106 | (10) |
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116 | (3) |
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4 Introduction to KA Adaptive Radar |
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119 | (40) |
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4.1 The Need for KA Radar |
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119 | (5) |
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4.2 Introduction to KA Radar: Back to "Bayes-ics" |
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124 | (4) |
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4.2.1 Indirect KA Radar: Intelligent Training and Filter Selection |
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126 | (2) |
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Example 4.1 Intelligent Filter Selection: Matching the Adaptive DoFs (ADoFs) to the Available Training Data |
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128 | (7) |
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4.2.2 Direct KA Radar: Bayesian Filtering and Data Prewhitening |
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131 | (4) |
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Example 4.2 Using Past Observations as a Prior Knowledge Source |
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135 | (4) |
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4.3 Real-Time KA Radar: The DARPA KASSPER Project |
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139 | (4) |
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4.3.1 Solution: Look-Ahead Scheduling |
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140 | (3) |
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Example 4.3 Balancing Throughput in a KASSPER HPEC Architecture |
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143 | (10) |
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4.3.2 Examples of a KA Architectures Developed by the DARPA/AFRL KASSPER Project |
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146 | (7) |
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153 | (2) |
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155 | (4) |
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5 Putting it All Together: CoFAR |
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159 | (10) |
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5.1 Cognitive Radar: The Fully Adaptive Knowledge-Aided Approach |
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159 | (5) |
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5.1.1 A Cognitive Radar Architecture for GMTT |
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160 | (2) |
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5.1.2 Informal Operational Narrative for a GMTI Radar |
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162 | (2) |
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5.2 CoFAR Radar Scheduler |
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164 | (2) |
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5.3 Areas for Future Research and Development |
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166 | (1) |
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167 | (2) |
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6 Cognitive Radar and Artificial Intelligence |
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169 | (8) |
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6.1 Relationship between Cognitive Radar and Artificial Intelligence |
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169 | (1) |
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6.2 Cognitive Radar Utilizing Traditional AI |
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170 | (1) |
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6.3 Cognitive Radar Utilizing Deep Learning AI |
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171 | (4) |
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6.3.1 CoFAR Mission Computer |
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173 | (1) |
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6.3.2 CoFAR Radar Controller and Scheduler |
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174 | (1) |
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175 | (1) |
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175 | (1) |
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176 | (1) |
| About the Author |
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177 | (2) |
| Index |
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179 | |
Lisainfo e-raamatute kohta