Discover the technology for the next generation of radar systems

Here is the first book that brings together the key concepts
essential for the application of Knowledge Based Systems (KBS) to
radar detection, tracking, classification, and scheduling. The book
highlights the latest advances in both KBS and radar signal and
data processing, presenting a range of perspectives and innovative
results that have set the stage for the next generation of adaptive
radar systems.

The book begins with a chapter introducing the concept of
Knowledge Based (KB) radar.

The remaining nine chapters focus on current developments and
recent applications of KB concepts to specific radar functions.
Among the key topics explored are:

* Fundamentals of relevant KB techniques

* KB solutions as they apply to the general radar problem

* KBS applications for the constant false-alarm rate processor

* KB control for space-time adaptive processing

* KB techniques applied to existing radar systems

* Integrated end-to-end radar signals

* Data processing with overarching KB control

All chapters are self-contained, enabling readers to focus on
those topics of greatest interest. Each one begins with
introductory remarks, moves on to detailed discussions and
analysis, and ends with a list of references. Throughout the
presentation, the authors offer examples of how KBS works and how
it can dramatically improve radar performance and capability.
Moreover, the authors forecast the impact of KB technology on
future systems, including important civilian, military, and
homeland defense applications.

With chapters contributed by leading international researchers
and pioneers in the field, this text is recommended for both
students and professionals in radar and sonar detection, tracking,
and classification and radar resource management.

Muralidhar Rangaswamy, PhD, IEEE Fellow, is the Technical Advisor for the Radar Signal Processing Branch at the Sensors Directorate of the Air Force Research Laboratory (AFRL). His research interests include radar signal processing, spectrum estimation, modeling non-Gaussian interference phenomena, and statistical communication theory. Dr. Rangaswamy has coauthored more than eighty refereed journal and conference papers. In addition, he is a contributor to three books and a coinventor on two U.S. patents.

Here is the first book that brings together the key concepts essential for the application of Knowledge Based Systems (KBS) to radar detection, tracking, classification, and scheduling. The book highlights the latest advances in both KBS and radar signal and data processing, presenting a range of perspectives and innovative results that have set the stage for the next generation of adaptive radar systems.

The book begins with a chapter introducing the concept of Knowledge Based (KB) radar.
The remaining nine chapters focus on current developments and recent applications of KB concepts to specific radar functions. Among the key topics explored are:

• Fundamentals of relevant KB techniques

• KB solutions as they apply to the general radar problem

• KBS applications for the constant false-alarm rate processor

• KB control for space-time adaptive processing

• KB techniques applied to existing radar systems

• Integrated end-to-end radar signals

• Data processing with overarching KB control

All chapters are self-contained, enabling readers to focus on those topics of greatest interest. Each one begins with introductory remarks, moves on to detailed discussions and analysis, and ends with a list of references. Throughout the presentation, the authors offer examples of how KBS works and how it can dramatically improve radar performance and capability. Moreover, the authors forecast the impact of KB technology on future systems, including important civilian, military, and homeland defense applications.

With chapters contributed by leading international researchers and pioneers in the field, this text is recommended for both students and professionals in radar and sonar detection, tracking, and classification and radar resource management.

Contributors xi

1 Introduction 1
Fulvio Gini and Muralidhar Rangaswamy

1.1 Organization of the Book 3

Acknowledgments 7

References 7

2 Cognitive Radar 9
Simon Haykin

2.1 Introduction 9

2.2 Cognitive Radar Signal-Processing Cycle 10

2.3 Radar-Scene Analysis 12

2.3.1 Statistical Modeling of Statistical Representation of Clutter- and Target-Related Information 13

2.4 Bayesian Target Tracking 14

2.4.1 One-Step Tracking Prediction 16

2.4.2 Tracking Filter 16

2.4.3 Tracking Smoother 18

2.4.4 Experimental Results: Case Study of Small Target in Sea Clutter 19

2.4.5 Practical Implications of the Bayesian Target Tracker 20

2.5 Adaptive Radar Illumination 21

2.5.1 Simulation Experiments in Support of Adjustable Frequency Modulation 22

2.6 Echo-Location in Bats 23

2.7 Discussion 25

2.7.1 Learning 27

2.7.2 Applications 27

2.7.2.1 Multifunction Radars 27

2.7.2.2 Noncoherent Radar Network 28

Acknowledgments 29

References 29

3 Knowledge-Based Radar Signal and Data Processing: A Tutorial Overview 31
Gerard T. Capraro, Alfonso Farina, Hugh D. Griffiths, and Michael C. Wicks

3.1 Radar Evolution 32

3.2 Taxonomy of Radar 34

3.3 Signal Processing 35

3.4 Data Processing 37

3.5 Introduction to Artificial Intelligence 38

3.5.1 Why Robotics and Knowledge-Based Systems? 39

3.5.2 Knowledge Base Systems (KBS) 39

3.5.3 Semantic Web Technologies 40

3.6 A Global View and KB Algorithms 40

3.6.1 An Airborne Autonomous Intelligent Radar System (AIRS) 42

3.6.2 Filtering, Detection, and Tracking Algorithms and KB Processing 44

3.7 Future work 49

3.7.1 Target Matched Illumination 49

3.7.2 Spectral Interpolation 49

3.7.3 Bistatic Radar and Passive Coherent Location 50

3.7.4 Synthetic Aperture Radar 50

3.7.5 Resource Allocation in a Multifunction Phased Array Radar 50

3.7.6 Waveform Diversity and Sensor Geometry 51

Acknowledgments 51

References 51

4 An Overview of Knowledge-Aided Adaptive Radar at DARPA and Beyond 55
Joseph R. Guerci and Edward J. Baranoski

4.1 Introduction 56

4.1.1 Background on STAP 56

4.1.2 Examples of Real-World Clutter 60

4.2 Knowledge-Aided STAP (KA-STAP) 61

4.2.1 Knowledge-Aided STAP: Back to Bayes-ics 61

4.2.1.1 Case I: Intelligent Training and Filter Selection (ITFS) 62

4.2.1.2 Case II: Bayesian Filtering and Data Pre-Whitening 63

4.3 Real-Time KA-STAP: The DARPA KASSPER Program 67

4.3.1 Obstacle…