Approximate series representations of linear operations on second-order stochastic processes: application to Simulation

Series representations of the more usual linear operations in weak sense on a second-order stochastic process are studied. The starting point of this analysis is the optimal Cambanis expansion of the stochastic process considered. Likewise, the extensions of the approximate series expansions based o...

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Bibliographic Details
Published in:IEEE transactions on information theory 2006-04, Vol.52 (4), p.1789-1794
Main Authors: Navarro-Moreno, J., Ruiz-Molina, J.C., Fernandez-Alcala, R.M.
Format: Article
Language:English
Subjects:
Applied sciences
Approximation
Autocorrelation
Computation
Computational modeling
Computer simulation
Eigenvalues and eigenfunctions
Equations
Exact sciences and technology
Information theory
Information, signal and communications theory
Kernel
Linear operations in weak sense
Mean square error methods
Optimization
Random variables
Representations
Series expansion
series representations of stochastic processes
Signal processing
simulation
Statistics
Stochastic processes
Telecommunications and information theory
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Summary:Series representations of the more usual linear operations in weak sense on a second-order stochastic process are studied. The starting point of this analysis is the optimal Cambanis expansion of the stochastic process considered. Likewise, the extensions of the approximate series expansions based on the Rayleigh-Ritz method are presented for such linear operations on the process. The main advantages of these extensions are that they are computationally feasible and entail a significant reduction in the computational burden. Finally, their applicability as a practical simulation tool is examined.
ISSN:0018-9448
1557-9654
DOI:10.1109/TIT.2006.871033