Spectral Representation of Discrete-Time Stationary Signals and Their Computer Simulations

Wojbor A. Woyczyński ()
Additional contact information
Wojbor A. Woyczyński: Case Western Reserve University, Department of Statistics and Center for Stochastic and Chaotic Processes in Sciences and Technology

Chapter Chapter 9 in A First Course in Statistics for Signal Analysis, 2011, pp 193-222 from Springer

Abstract: Abstract Given an arbitrary power spectrum S X (f) or, equivalently, its inverse Fourier transform, the autocovariance function γx(τ), our ability to simulate the corresponding stationary random signals X(t), using only the pseudo-random number generator, which produces, say, discrete-time white noise, depends on the observation that, in some sense, all stationary random signals can be approximated by superpositions of random harmonic oscillations such as those discussed in Examples 4.1.2 and 4.1.9.

Keywords: Power Spectrum; White Noise; Spectral Density; Power Spectral Density; Spectral Representation (search for similar items in EconPapers)
Date: 2011
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-0-8176-8101-2_9

Ordering information: This item can be ordered from
http://www.springer.com/9780817681012

DOI: 10.1007/978-0-8176-8101-2_9

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().