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, estima...
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.
