Knowledge Based Radar Detection, Tracking and Classification
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More About This Title Knowledge Based Radar Detection, Tracking and Classification


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.


Fulvio Gini, PhD, IEEE Fellow, is a Full Professor at the University of Pisa, Italy. He was the technical program cochairman of the 2006 EURASIP Signal and Image Processing Conference (Florence, Italy) and the 2008 IEEE Radar Conference (Rome, Italy). His research interests include radar signal processing; cyclostationary signal analysis; non-Gaussian signal modeling, detection, and estimation; and parameter estimation and data extraction from multichannel interferometric SAR data. Professor Gini has coauthored more than eighty refereed journal papers, more than eighty conference papers, and three book chapters.

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.



1. Introduction (Fulvio Gini and Muralidhar Rangaswamy).

2. Cognitive Radar (Simon Haykin)

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

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

5. Space-Time Adaptive Processing for Airborne Radar: A Knowledge-Based Perspective (Michael C. Wicks, Muralidhar Rangaswamy, Raviraj S. Adve, and Todd B. Hale).

6. CFAR Knowledge-Aided Radar Detection and its Demonstration Using Measured Airborne Data (Christopher T. Capraro, Gerard T. Capraro, Antonio De Maio, Alfonso Farina, and Michael C. Wicks).

7. STAP via Knowledge-Aided Covariance Estimation and the FRACA Meta-Algorithms (Shannon D. Blunt, Karl Gerlach, Muralidhar Rangaswamy, and Aaron K. Shackelford).

8. Knowledge-Based Radar Tracking (Alessio Benavoli, Luigi Chisci, Alfonso Farina, Sandro Immediata, and Luca Timmoneri).

9. Knowledge-Based Radar Target Classification (Igal Bilik and Joseph Tabrikian).

10. Multifunction Radar Resource Management (Sergio Luis de Carvalho Miranda, Chris J. Baker, Karl Woodbridge and Hugh D. Griffiths).