Download Generalizations of Cyclostationary Signal Processing: by Antonio Napolitano PDF

By Antonio Napolitano

The relative movement among the transmitter and the receiver modifies the nonstationarity houses of the transmitted sign. particularly, the almost-cyclostationarity estate exhibited via just about all modulated signs followed in communications, radar, sonar, and telemetry may be remodeled into extra normal sorts of nonstationarity. a formal statistical characterization of the bought sign enables the layout of sign processing algorithms for detection, estimation, and type that considerably outperform algorithms in keeping with classical descriptions of signals.Generalizations of Cyclostationary sign Processing addresses those matters and contains the next key features: 

  • Presents the underlying theoretical framework, followed via information in their sensible program, for the mathematical versions of generalized almost-cyclostationary procedures and spectrally correlated strategies; sessions of signs discovering transforming into significance in components akin to cellular communications, radar and sonar.
  • Explains moment- and higher-order characterization of nonstationary stochastic procedures in time and frequency domains.
  • Discusses non-stop- and discrete-time estimators of statistical capabilities of generalized almost-cyclostationary methods and spectrally correlated processes.
  • Provides research of mean-square consistency and asymptotic Normality of statistical functionality estimators.
  • Offers huge research of Doppler channels due to the relative movement among transmitter and receiver and/or surrounding scatterers.
  • Performs sign research utilizing either the classical stochastic-process technique and the practical method, the place statistical services are equipped ranging from a unmarried functionality of time.

Content:
Chapter 1 historical past (pages 1–43):
Chapter 2 Generalized Almost?Cyclostationary techniques (pages 45–121):
Chapter three enhances and Proofs on Generalized Almost?Cyclostationary approaches (pages 123–179):
Chapter four Spectrally Correlated procedures (pages 181–290):
Chapter five enhances and Proofs on Spectrally Correlated procedures (pages 291–354):
Chapter 6 useful procedure for sign research (pages 355–379):
Chapter 7 purposes to cellular Communications and Radar/Sonar (pages 381–464):
Chapter eight Bibliographic Notes (pages 465–468):

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Extra resources for Generalizations of Cyclostationary Signal Processing: Spectral Analysis and Applications

Sample text

As examples of GACS signals, the chirp signal and a nonuniformly sampled signal are considered and their generalized cyclic statistics are derived. In (Izzo and Napolitano 2002a) and (Izzo and Napolitano 2002b), it is shown that several time-variant channels of interest in communications transform a transmitted ACS signal into a GACS one. In particular, in (Izzo and Napolitano 2002b) it is shown that the GACS model can be appropriate to describe the output signal of Doppler channels when the input signal is ACS and the channel introduces a quadratically time-variant delay.

2000). In (Gardner et al. 2006) and references therein, applications of cyclostationarity for interference tolerant channel identification and equalization, signal detection and classification, source separation, periodic autoregressive (AR) and autoregressive moving-average (ARMA) modeling and prediction, are described. The spectral line regeneration property of ACS signal can be exploited for synchronization purposes (Gardner 1987d). Cyclostationarity properties can be suitably exploited in minimum mean-square error (MMSE) linear filtering or Wiener filtering.

YN ]T . Let us consider, now, the N-dimensional complex-valued column vector Z = [Z1 , . . , ZN ]T X + jY . It is characterized by the same 2Nth-order joint pdf fV (v) of the 2N-dimensional real-valued vector V defined above. 200) 42 Generalizations of Cyclostationary Signal Processing can be considered. 208) with CZZ∗ and CZZ referred to as covariance matrix and conjugate covariance matrix, respectively. The Nth-order cumulant of the complex random variables Z1 , . . , ZN can be defined as (Spooner and Gardner 1994, App.

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