Cyclostationary Processes and Time Series Theory, Applications, and Generalizations

Langue : Anglais

Auteur : Napolitano Antonio

Couverture de l’ouvrage Cyclostationary Processes and Time Series

Résumé
Sommaire
Biographie
Commentaire

Many processes in nature arise from the interaction of periodic phenomena with random phenomena. The results are processes that are not periodic, but whose statistical functions are periodic functions of time. These processes are called cyclostationary and are an appropriate mathematical model for signals encountered in many fields including communications, radar, sonar, telemetry, acoustics, mechanics, econometrics, astronomy, and biology.

Cyclostationary Processes and Time Series: Theory, Applications, and Generalizations addresses these issues and includes the following key features.

PART I CYCLOSTATIONARITY 1. Characterization of Stochastic Processes 2. Characterization of Time-Series 3 Almost-Cyclostationary Signal Processing 4. Higher-Order Cyclostationarity 5. Ergodic Properties and Measurement of Characteristics 6. Quadratic Time-Frequency Distributions 7. Manufactured Signals 8. Detection and Cycle Frequency Estimation 9. Communications Systems 10. Selected Topics and Applications

PART II GENERALIZATIONS 11. Limits of the Almost-Cyclostationary Model 12. Generalized Almost-Cyclostationary Signals 13. Spectrally Correlated Signals 14. Oscillatory Almost-Cyclostationary Signals 15. The Big Picture

PART III APPENDICES A. Nonstationary Signal Analysis B. Almost-Periodic Functions C. Sampling and Replication D. Hilbert Transform, Analytic Signal, and Complex Envelope E. Complex Random Vectors, Quadratic Forms, and Chi Squared Distribution F. Bibliographic Notes

Antonio Napolitano is Full Professor of Telecommunications at the University of Napoli Parthenope (Italy). In 1995 he received the Best Paper of the Year Award from the European Association for Signal Processing (EURASIP) for a paper on higher-order cyclostationarity. In 2007 was recipient of the EURASIP Best Paper Award for a paper on the functional approach in signal analysis. In 2008 he received from Elsevier the Most Cited Paper Award for a review article on cyclostationarity.
In 2016 he became an IEEE Fellow. He has been Associate Editor of the IEEE Transactions on Signal Processing and is on the Editorial Board of Signal Processing (Elsevier) and Digital Signal Processing (Elsevier). He has been in the Signal Processing Theory and Methods Technical Committees (SPM-TC) and is now in the Sensor Array and Multichannel Technical Committee (SAM-TC) of the IEEE Signal Processing Society.

Presents the foundations and developments of the second- and higher-order theory of cyclostationary signals
Performs signal analysis using both the classical stochastic process approach and the functional approach for time series
Provides applications in signal detection and estimation, filtering, parameter estimation, source location, modulation format classification, and biological signal characterization
Includes algorithms for cyclic spectral analysis along with Matlab/Octave code
Provides generalizations of the classical cyclostationary model in order to account for relative motion between transmitter and receiver and describe irregular statistical cyclicity in the data