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
