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The relative motion between the transmitter and the receiver modifies the nonstationarity properties of the transmitted signal. In particular, the almostcyclostationarity 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 almostcyclostationary processes and spectrally correlated processes; two classes of signals finding growing importance in areas such as mobile communications, radar and sonar. Explains second and higherorder characterization of nonstationary stochastic processes in time and frequency domains. Discusses continuous and discretetime estimators of statistical functions of generalized almostcyclostationary processes and spectrally correlated processes. Provides analysis of meansquare 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 stochasticprocess approach and the functional approach, where statistical functions are built starting from a single function of time.
⚠️ WARNING (California Proposition 65):
This product may contain chemicals known to the State of California to cause cancer,
birth defects, or other reproductive harm.
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