Cyclostationary Processes and Time Series - 1st Edition - ISBN: 9780081027080

Cyclostationary Processes and Time Series

1st Edition

Theory, Applications, and Generalizations

Editors: Antonio Napolitano
Paperback ISBN: 9780081027080
Imprint: Academic Press
Published Date: 1st November 2019
Page Count: 325
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Cyclostationary Processes and Time Series: Theory, Applications, and Generalizations gives engineers a good understand of cyclostationarity theory and the algorithms that can be used with traditional signal processing algorithms to optimize performance.

Key Features

  • Presents the only book on classical cyclostationary theory and recent theoretical advances
  • Provides detailed applications in signal detection and estimation, filtering, parameter estimation, source location, system identification and modulation format
  • Includes algorithms for cyclic spectral analysis, along with MATLAB/Octave code


Researchers and graduate students in electronic engineering, Stochastic Processes, Harmonic analysis, and Applied Mathematics

Table of Contents


1 General properties and structure of stochastic processes and time-series
1.1. Introduction
1.2. Stochastic processes
1.2.1. Continuous-time processes
1.2.2. Discrete-time processes
1.3. Time series
1.3.1. Continuous-time time series
1.3.2. Discrete-time time series
1.4. Link between the stochastic and fraction-of-time approaches
1.5. Complex processes and time series
1.6. Linear filtering
1.6.1. Structure of linear almost-periodically time-variant systems
1.6.2. Input/output relations in terms of cyclic statistic
1.7. Product modulation
1.8 Rice’s representation
1.9. Supports of cyclic spectra of band limited signals
1.10. Sampling and aliasing
1.11. Representations by stationary components
1.11.1. Continuous-time processes and time series
1.11.2. Discrete-time processes and time series
1.12 Multirate processing
1.13 Quadratic time-frequency distributions
1.14 Higher-order cyclostationarity

2 Ergodic properties and measurement of characteristics
2.1. Estimation of the cyclic autocorrelation function
2.2 Estimation of the cyclic spectrum
2.3. Two alternative approaches to the analysis of measurements on time series

3 Manufactured signals: modelling and analysis
3.1. Examples of communication signals
3.1.1. Double side-band amplitude-modulated signal
3.1.2. Pulse-amplitude-modulated signal
3.1.3 QAM signal
3.1.4. Direct-sequence spread-spectrum signal
3.1.5 CPM, GMSK signal
3.1.6 GPS signal
3.1.7 OFDM, LTE signals

4 Communications systems: analysis and design
4.1. Cyclic Wiener filtering
4.2. Synchronization
4.2.1. Spectral line generation
4.3 Signal parameter and waveform estimation
4.4 Channel identification and equalization
4.5 LTI-system identification with noisy-measurements
4.6. Blind LTI-system identification and equalization
4.7 Nonlinear-system identification

5 Signal detection and source location
5.1.Spectral line regeneration
5.2 Maximum likelihood detection and source location
5.3 Statistical test for presence of cyclostationarity
5.4 Statistical test for presence of spectral coherence
5.5 Subsampling-based significance test
5.6 Robust detectors
5.7 Higher-order statistic based detectors
5.8 Cycle frequency estimation

6 Periodic AR and ARMA modelling and prediction
Compressive sensing
Cyclostationary random fields
Applications to circuits, systems, and control .
Applications to acoustics and mechanics
Applications to econometrics
Applications to biology
Other applications

7 Doppler effect and nonstationarity
7.1 Doppler effect on ACS signals and limits of the ACS model
7.2 Second-order characterization of nonstationary stochastic processes

8 Generalized almost-cyclostationary processes
8.1 Second-order characterization
8.2 Estimation of the cyclic autocorrelation function
8.3 Discrete-time processes
8.4 Signal detection
8.5 Examples and applications
8.5.1 Doppler channel due to constant relative radial acceleration between transmitter and receiver
8.5.2 Communications signals with slowly time-variant parameters

9 Spectrally correlated processes
9.1.Second-order characterization
9.2 Estimation of the spectral correlation density function
9.3 Discrete-time processes
9.4 Signal detection
9.5 Examples and applications
9.5.1 Multipath Doppler channel
9.5.2 Moving source location
9.5.3 Fractional Brownian motion
9.5.4 Multirate processing
9.5.5 Spectral analysis with non uniform frequency spacing of ACS signals 

10. Oscillatory almost-cyclostationary processes
10.1 Second-order characterization
10.2 Examples and applications
10.2.1 LTV filtering of ACS processes
10.2.2 Modulated cyclical processes
10.2.3 Amplitude-modulated and frequency warped signals
10.2.4 ECG signal


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© Academic Press 2020
Academic Press
Paperback ISBN:

About the Editor

Antonio Napolitano

Antonio Napolitano is Full Professor of Telecommunications at the University of Napoli Parthenope. In 1995 he received the Best Paper of the Year Award from the European Association for Signal Processing (EURASIP) for a paper on higher-order cyclostationarity. In 2007 was recipient of the EURASIP Best Paper Award for a paper on the functional approach in signal analysis. In 2008 he received from Elsevier the Most Cited Paper Award for a review article on cyclostationarity. In 2016 he became an IEEE Fellow. He has been Associate Editor of the IEEE Transactions on Signal Processing and is on the Editorial Board of Signal Processing (Elsevier) and Digital Signal Processing (Elsevier).

Affiliations and Expertise

Full Professor of Telecommunications at the University of Napoli Parthenope

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