Cyclostationary Processes and Time Series

Released on by Antonio Napolitano
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  • Cyclostationary Processes and Time Series Book Detail

  • Author : Antonio Napolitano
  • Release Date : 2019-10-24
  • Publisher : Academic Press
  • Genre : Technology & Engineering
  • Pages : 626
  • ISBN 13 : 0081027370
  • File Size : 27,27 MB
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Cyclostationary Processes and Time Series by Antonio Napolitano PDF Summary

Book Description: Many processes in nature arise from the interaction of periodic phenomena with random phenomena. The results are processes that 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 many fields including communications, radar, sonar, telemetry, acoustics, mechanics, econometrics, astronomy, and biology. Cyclostationary Processes and Time Series: Theory, Applications, and Generalizations addresses these issues and includes the following key features. Presents the foundations and developments of the second- and higher-order theory of cyclostationary signals Performs signal analysis using both the classical stochastic process approach and the functional approach for time series Provides applications in signal detection and estimation, filtering, parameter estimation, source location, modulation format classification, and biological signal characterization Includes algorithms for cyclic spectral analysis along with Matlab/Octave code Provides generalizations of the classical cyclostationary model in order to account for relative motion between transmitter and receiver and describe irregular statistical cyclicity in the data

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Cyclostationary Processes and Time Series

Cyclostationary Processes and Time Series

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Many processes in nature arise from the interaction of periodic phenomena with random phenomena. The results are processes that are not periodic, but whose stat

Periodically Correlated Random Sequences

Periodically Correlated Random Sequences

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Uniquely combining theory, application, and computing, this bookexplores the spectral approach to time series analysis The use of periodically correlated (or cy