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Statistical Signal Processing of Complex-valued Data


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Table of Contents

1. The origins and uses of complex signals; 2. Introduction to complex random vectors and processes; 3. Second-order description of complex random vectors; 4. Correlation analysis; 5. Estimation; 6. Performance bounds for parameter estimation; 7. Detection; 8. Wide-sense stationary processes; 9. Nonstationary processes; 10. Cyclostationary processes; Appendix A. Rudiments of matrix analysis; Appendix B. Complex differential calculus (Wirtinger calculus); Appendix C. Introduction to majorization.

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Provides the tools and algorithms needed to deal with improper and noncircular complex signals, and highlights the resulting real-world payoffs.

About the Author

Peter J. Schreier is a Senior Lecturer in the School of Electrical Engineering and Computer Science, The University of Newcastle, Australia. He received his Ph.D. in electrical engineering from the University of Colorado at Boulder in 2003. He currently serves on the Editorial Board of the IEEE Transactions on Signal Processing, and on the IEEE Technical Committee of Machine Learning for Signal Processing. Louis L. Scharf is Professor of Electrical and Computer Engineering and Statistics at Colorado State University. He received his Ph.D. from the University of Washington at Seattle in 1969. He has since received numerous awards for his research contributions to statistical signal processing, including an IEEE Distinguished Lectureship, an IEEE Third Millennium Medal, and the Technical Achievement and Society Awards from the IEEE Signal Processing Society. He is a Life Fellow of the IEEE.


"This book must be in the personal library of everyone who deals with random complex variables." Guy Jumarie, Mathematical Reviews

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