Provides the tools and algorithms needed to deal with improper and noncircular complex signals, and highlights the resulting real-world payoffs.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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.
Inhaltsangabe
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.
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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