Model-Based Parameter-Estimation Applications in Electromagnetics

Miller, E. K.

doi:10.1007/978-94-011-3232-9_12

Cited by 9 publications

(9 citation statements)

References 31 publications

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“…The MBPE approach enables minimizing the number of samples of a spectrum or pattern needed to develop their continuous representation using a simple, reduced-order fitting-model (FM). As originally proposed [1][2], using overlapping data windows is a reliable and effective way to determine where additional data samples are needed to satisfy a desired accuracy. About 4 data samples per resonance are needed to model a spectrum to an uncertainty of 1%.…”

Section: Concluding Commentsmentioning

confidence: 99%

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Adaptive windowed estimation of electromagnetic frequency spectra and radiation patterns to a specified uncertainty

Miller

2013

2013 IEEE Digital Signal Processing and Signal Processing Education Meeting (DSP/SPE)

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Common to areas involving waves and fields like acoustics and electromagnetics is the need to evaluate frequency spectra and far-field radiation patterns. This has usually been done on a point-wise basis, sampling a first-principles or generating model (GM) finely enough to ensure that important features such as narrow resonances and nulls are not missed. This can lead to many more samples, and consequently greater computational cost, than is really needed. Described below is an approach that employs a fitting model (FM) derived from model-based parameter estimation together with multiple, overlapping frequency-or angleobservation windows. Comparing FM samples in the overlapping windows shows where additional GM samples are needed to achieve a desired uncertainty in the computed results while minimizing the number of GM samples required.

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Section: Concluding Commentsmentioning

confidence: 99%

“…Some examples of their application are then included followed by some concluding comments. The basic approach of using windowed, overlapping FMs was discussed in [1][2].…”

Section: Introductionmentioning

confidence: 99%

Adaptive windowed estimation of electromagnetic frequency spectra and radiation patterns to a specified uncertainty

Miller

2013

2013 IEEE Digital Signal Processing and Signal Processing Education Meeting (DSP/SPE)

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“…22 [79]. The model in this case is a low-order rational function whose numerator and denominator orders are n and d respectively.…”

Section: Modeling Ground Screens--mostmentioning

confidence: 99%

Low-frequency computational electromagnetics for antenna analysis

Burke¹

1992

Proc. IEEE

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Proc. IEEE '91 DISCLAIMERThis report was prepared as an account of work sponsoredby an agency of the United States Government. Neither the United States Government nor any a_.-nc? thereof, nor any of their employees, makes any warranty, expressor implied, or assumesany legal liability or responsi-,. bility for the accuracy, completeness, or usef, lness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Referenc¢ herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise dots not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.By acceptance of ims art,cia, the pubhsher recogmzes that the U S Government retains a nonexclumve,royalty.free I=censeto DulohShor reproduce the pubhshed form Of th=SCOntr_button.or to allow others tO dO So. for U.S Government purposesThe Los Alamos Nat,anal LabOratory requests that the pubhsher ,dentJty tins art_cleas work performed under theausp, ABSTRACT An overview of low-frequency, compuational metheds for modeling the electromagnetic characteristics of antennas is presented here. The article presents a brief analytical background, and summarizes the essential ingredients of the method of moments, for numerically solving low.frequency antenna problems. Some extensions to the basic models of perfectly conducting objects in free space are also summarized, followed by a consideration of some of the some computational issues that affect model accuracy, efficiency and utility. A variety of representative computations are then presented to illustrate various modeling aspects and capabilities that are currently available. A fairly extensive bibliography is included to suggest further reference material to the reader.Low-Frequency Antenna Models, Proc. IEEE '91,

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“…In this paper, we present a model-based parameter estimation (henceforth MBPE) approach [12], to obtain fairly accurate approximations for both the dispersion law and the field distribution of single-mode fibers, with a minimum storage and CPU budget.…”

Section: Introductionmentioning

confidence: 99%

A model-based parameter estimation approach for numerical analysis of single-mode optical fibers

Fiumara

Galdi

Pierro

et al. 1999

J. Lightwave Technol.

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An efficient numerical method is proposed and implemented for the analysis of propagation characteristics of single-mode optical fibers with arbitrary refractive index profile. The method follows the concept of the so-called model-based parameter estimation, and the Padé algorithm is used to construct a low-order rational approximant of the spectral domain Green's function, obtained by solving a hierarchy of static problems. The sought eigenfrequency and field distribution are then estimated by computing, respectively, the dominant pole and the related residual of the rational approximant. A number of profiles are analyzed and experiments show that very accurate results can be cheaply obtained through this technique. Index Terms-Model-based parameter estimation, optical fibers, Padé approximants.

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Model-Based Parameter-Estimation Applications in Electromagnetics

Cited by 9 publications

References 31 publications

Adaptive windowed estimation of electromagnetic frequency spectra and radiation patterns to a specified uncertainty

Adaptive windowed estimation of electromagnetic frequency spectra and radiation patterns to a specified uncertainty

Low-frequency computational electromagnetics for antenna analysis

A model-based parameter estimation approach for numerical analysis of single-mode optical fibers

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