Many processes in nature arise from the interaction of periodic phenomena with random phenomena. The results are processes which 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 telecommunications, radar, sonar, telemetry, astronomy, mechanics, econometric, biology. In contrast, the classical model of stationary processes considers statistical functions which do not depend on time. More generally, if different periodicities are present in the generation mechanism of the process, the process is called almost cyclostationary (ACS). Almost all modulated signals adopted in communications, radar, and sonar can be modeled as ACS. Thus, in the past twenty years the exploitation of almost-cyclostationarity properties in communications and radar has allowed the design of signal processing algorithms for detection, estimation, and classification that significantly outperform classical algorithms based on a stationary description of signals. The gain in performance is due to a proper description of the nonstationarity of the signals, that is, the time variability of their statistical functions.

In this book, mathematical models for two general classes of nonstationary processes are presented: generalized almost-cyclostationary (GACS) processes and spectrally correlated (SC) processes. Both classes of processes include cyclostationary and ...

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