Cognitive Radar
The Knowledge-Aided Fully Adaptive Approach
1st Edition
Published on 1. January 2010
190 pages
978-1-59693-365-1 (ISBN)
Description
Cognitive radar (CR) refers to radar that is capable of interacting intelligently with its environment by adapting both its transmit and receive functions based on contextual awareness and expert reasoning. Chronicling the new field, this cutting-edge resource provides an accessible introduction to the theory and applications of CR, and presents a comprehensive overview of the latest developments in this emerging area. The first book on the subject, this unique volume covers important breakthroughs in advanced radar systems, and offers new and powerful methods for combating difficult clutter environments. Professionals find details on specific algorithmic and real-time high-performance embedded computing (HPEC) architectures. This practical book is supported with numerous examples that clarify key topics, and includes more than 370 equations.
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04/2010
Artech House Publishers
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Content
- Cognitive Radar: The Knowledge-Aided Fully Adaptive Approach
- Contents
- Preface
- Chapter 1 Introduction
- 1.1 Why "Cognitive" Radar?
- 1.2 Functional Elements and Characteristics of a Cognitive Radar Architecture
- 1.2.1 Adaptive Transmit Capability
- 1.2.2 Knowledge-Aided Processing
- 1.3 Book Organization
- References
- Chapter 2 Optimum Multi-Input Multioutput(MIMO) Radar
- 2.1 Introduction
- 2.2 Jointly Optimizing the Transmit and Receive Functions Case I: Maximizing SINR
- Example 2.1 Multipath Interference
- 2.3 Jointly Optimizing the Transmit and Receive Functions Case II: Maximizing Signal-to-Clutter
- Example 2.2 Sidelobe Target Suppression: "Sidelobe Nulling on Transmit"
- Example 2.3 Optimal Pulse Shape for Maximizing SCR
- Example 2.4 Optimum Space-Time MIMO Processing for Clutter Suppression in Airborne MTI Radar
- 2.4 Optimum MIMO Target Identification
- Example 2.5 Two-Target Identification Example
- Example 2.6 Multitarget Identification Example
- 2.5 Constrained Optimum MIMO Radar
- Example 2.7 Prenulling on Transmit
- Example 2.8 Relaxed Projection Example
- Example 2.9 Nonlinear FM (NLFM) to Achieve Constant Modulus
- Example 2.10 Matched Subspace Example
- Appendix 2.A: Infinite Duration (Steady State) Case
- References
- Chapter 3 Adaptive Multi-Input Multioutput (MIMO) Radar
- 3.1 Introduction
- 3.2 Transmit-Independent Channel Estimation
- Example 3.1 Adaptive Multipath Interference Mitigation
- 3.3 Dynamic MIMO Calibration
- Example 3.2 MIMO Cohere-on-Target
- 3.4 Transmit-Dependent Channel Estimation
- Example 3.3 STAP-on-Transmit (STAP-Tx) Example
- Example 3.4 DDMA MIMO STAP Clutter Mitigation Example for GMTI Radar
- 3.5 Theoretical Performance Bounds of the DDMA MIMOSTAP Approach
- References
- Chapter 4 Introduction to Knowledge-Aided (KA) Adaptive Radar
- 4.1 The Need for KA Radar
- 4.2 Introduction to KA Radar: Back to "Bayes-ics"
- 4.2.1 Indirect KA Radar: Intelligent Training and Filter Selection
- Example 4.1 Intelligent Filter Selection: Matching the Adaptive DoFs(ADoFs) to the Available Training Data
- 4.2.2 Direct KA Radar: Bayesian Filtering and Data Prewhitening
- Example 4.2 Using Past Observations as a Prior Knowledge Source
- 4.3 Real-Time KA Radar: The DARPA KASSPER Project
- 4.3.1 Solution: Look-Ahead Scheduling
- Example 4.3 Balancing Throughput in a KASSPER HPEC Architecture
- 4.3.2 Examples of a KA Architectures Developed by the DARPA/AFRL KASSPER Project
- 4.4 KA Radar Epilogue
- References
- Chapter 5 Putting It All Together
- 5.1 Cognitive Radar: The Fully Adaptive Knowledge-Aided Approach
- Example 5.1: A Cognitive Radar Architecture
- 5.1.1 Informal Operational Narrative for a GMTI Radar
- 5.2 Areas for Future Research and Development
- References
- About the Author
- Index
