Quantification of high dimensional uncertainty in propagation over random terrain

Ozbayat, Selman; Janaswamy, Ramakrishna

doi:10.1109/aps.2013.6711465

Cited by 2 publications

(1 citation statement)

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“…A forward scattering approach in computing PF far from the transmitter location, whereas the randomness is in the generation of the randomly shaped perfectly conducting (PEC) surface, has conventionally been tackled with MC sampling [43], [46]. We have considered this application with sparse grids in [47]. There, due to highly nonsmooth behavior in the random space and high-feature of existing random surface generation tools, the sparse grid performances are poorer than that of QMC methods in general.…”

Section: Discussionmentioning

confidence: 99%

Assessment of Adaptive Sparse Grid Collocation Methods in Wave Propagation Environments With Uncertainty

Ozbayat

Janaswamy

2014

IEEE Trans. Antennas Propagat.

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Sparse grid collocation methods are used for uncertainty quantification in electromagnetic propagation problems. The first application here involves waves propagating in lossy media with uncertain permittivities and permeabilities, for which several cases with increasing random-space dimensionality are exemplified. The objective in the second application is to compute expected signal strength above flat Earth surface at ranges far from transmitter location, where randomness is present due to uncertain refractivity of the atmosphere. Two different sparse grid algorithms are demonstrated throughout the paper, and the deterministic evaluators are accessed as a black box by the sparse grid algorithms. The difficult task of a priori method adaption is overcome by a first order check that enforces the black box solver only a few times. Through the results considered, strengths of the two algorithms are differentiated depending on the characteristics of the randomness. The advantage of a new routine used in this work is emphasized by showing its numerical contribution to the field estimation problem in a specific example. By means of the sparse grid methods used, we quantitatively provide the relative importance of each RV in contributing to the changes in the field distribution received at the observation range.Index Terms-Inhomogeneous atmosphere, parabolic equation method, radiowave propagation, random media, sparse grid collocation, split-step Fourier propagator, uncertainty quantification.

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Section: Discussionmentioning

confidence: 99%

Assessment of Adaptive Sparse Grid Collocation Methods in Wave Propagation Environments With Uncertainty

Ozbayat

Janaswamy

2014

IEEE Trans. Antennas Propagat.

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Techniques to Increase Computational Efficiency in Some Deterministic and Random Electromagnetic Propagation Problems

Ozbayat¹

2013

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Quantification of high dimensional uncertainty in propagation over random terrain

Cited by 2 publications

References 5 publications

Assessment of Adaptive Sparse Grid Collocation Methods in Wave Propagation Environments With Uncertainty

Assessment of Adaptive Sparse Grid Collocation Methods in Wave Propagation Environments With Uncertainty

Techniques to Increase Computational Efficiency in Some Deterministic and Random Electromagnetic Propagation Problems

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