Cyclostationary Processes and Time Series: Theory, Applications, and Generalizations

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.

Presents the foundations and developments of the second- and higher-order theory of cyclostationary signalsPerforms signal analysis using both the classical stochastic process approach and the functional approach for time seriesProvides applications in signal detection and estimation, filtering, parameter estimation, source location, modulation format classification, and biological signal characterizationIncludes algorithms for cyclic spectral analysis along with Matlab/Octave codeProvides 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

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