Generalizations of Cyclostationary Signal Processing
Spectral Analysis and Applications
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Addressing the problem of consistent statistical-function estimation for two classes (GACS and SC) of nonstationary processes, this book is organized for readers with different prerequisites. The first three chapters are intended for readers to grasp the main ideas, and contain results in the form of theorems with sketches of proofs and illustrative examples. The last chapters present two-part mathematical proofs: the first part consists of formal manipulations, aimed at advanced readers such as engineering graduate students; the second consists of justification of the formal manipulations, for specialists such as mathematicians.
The relative motion between the transmitter and the receiver modifies the nonstationarity properties of the transmitted signal. In particular, the almost-cyclostationarity property exhibited by almost all modulated signals adopted in communications, radar, sonar, and telemetry can be transformed into more general kinds of nonstationarity. A proper statistical characterization of the received signal allows for the design of signal processing algorithms for detection, estimation, and classification that significantly outperform algorithms based on classical descriptions of signals. Generalizations of Cyclostationary Signal Processing addresses these issues and includes the following key features:
Presents the underlying theoretical framework, accompanied by details of their practical application, for the mathematical models of generalized almost-cyclostationary processes and spectrally correlated processes; two classes of signals finding growing importance in areas such as mobile communications, radar and sonar.
Explains second- and higher-order characterization of nonstationary stochastic processes in time and frequency domains.
Discusses continuous- and discrete-time estimators of statistical functions of generalized almost-cyclostationary processes and spectrally correlated processes.
Provides analysis of mean-square consistency and asymptotic Normality of statistical function estimators.
Offers extensive analysis of Doppler channels owing to the relative motion between transmitter and receiver and/or surrounding scatterers.
Performs signal analysis using both the classical stochastic-process approach and the functional approach, where statistical functions are built starting from a single function of time.
Presents the underlying theoretical framework, accompanied by details of their practical application, for the mathematical models of generalized almost-cyclostationary processes and spectrally correlated processes; two classes of signals finding growing importance in areas such as mobile communications, radar and sonar.
Explains second- and higher-order characterization of nonstationary stochastic processes in time and frequency domains.
Discusses continuous- and discrete-time estimators of statistical functions of generalized almost-cyclostationary processes and spectrally correlated processes.
Provides analysis of mean-square consistency and asymptotic Normality of statistical function estimators.
Offers extensive analysis of Doppler channels owing to the relative motion between transmitter and receiver and/or surrounding scatterers.
Performs signal analysis using both the classical stochastic-process approach and the functional approach, where statistical functions are built starting from a single function of time.
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