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Space-Time Codes and MIMO Systems

Space-Time Codes and MIMO Systems by J.R. Guerci
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Whether you are a radar engineer looking to apply effective STAP(space-time adaptive processing) techniques to your system, or a non-radar specialist interested in important applications of multichannel adaptive filtering, this practical resource, based on a time-tested course taught in industry, government and academia, is essential reading. The book introduces you to basic STAP concepts and methods, placing emphasis on implementation in real-world systems.

You find the analysis tools you need to assess the impact of STAP on a variety of important radar applications. A toolkit of STAP algorithms and implementation techniques allows you the flexibility of adapting the best methods to your application. From important factors affecting STAP performance, to reduced-rank and minimized sample support STAP, the book clearly explains critical topics in a manner that is easily understandable to anyone with a basic background in radar and signal processing. Moreover, this unique reference includes detailed descriptions of the latest developments in STAP research, helping you keep up with important trends in the industry.


Introduction - Need for Space-Time and Multidimensional Processing in Modern Radar. Optimum vs. Adaptive Processing. A Brief History of Adaptive Processing in Radar. Outline.

1D Optimum and Adaptive Radar Processing - Optimum Spatial (Angle) Beamforming. Optimum Temporal (Doppler/Pulse) Processing. Adaptive 1D Processing. Adaptivity in Nonstationary Environments. Summary.

Space-Time Adaptive Processing - Need for Joint Space and Time Processing. Optimum Space-Time Processing for MTI Radar. Space-Time Adaptive Processing.

Important Factors Affecting STAP Performance - Channel Mismatch. Interference Subspace Leakage. Antenna Misalignment. Nonlinear Arrays. Interference Nonstationarity.

Reduced-Rank and Minimized Sample Support STAP - Need for Reduced-Rank STAP. A Taxonomy of Reduced-Rank Methods. Data Independent Reduced-Rank STAP. Data Dependent Reduced-Rank STAP. Structured Covariance Methods. Comparison Results.

Other Topics - Statistical Basis for STAP. Real-Time Implementation. Current and Future Research Trends in STAP for Radar.



J. R. Guerci is deputy director, special projects office for The Defense Advanced Research Projects Agency (DARPA). A Senior Member of the IEEE and a Member of the IEEE Radar Systems Panel, Dr. Guerci earned his Ph.D. in system engineering at the Polytechnic University, New York.

Artech House; July 2003
203 pages; ISBN 9781580536998
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Title: Space-Time Codes and MIMO Systems
Author: J.R. Guerci