Microwave structure characterization by a combination of FDTD and system identification methods

Houshmand, B.; Huang, Tian-Wei; Itoh, T.

doi:10.1109/75.242221

Cited by 26 publications

(4 citation statements)

References 7 publications

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“…The results obtained from the multigrid approach are compared to those ones obtained from a FDTD on a merely fine grid. The difference is characterized by means of the mean relative error (3) where and represent the phasors obtained by the proposed multigrid and by a merely fine-grid approach, respectively. Fig.…”

Section: Numerical Resultsmentioning

confidence: 99%

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A simple multigrid approach for calculating time-harmonic fields with FDTD

Klinkenbusch

2005

IEEE Microw. Wireless Compon. Lett.

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A coarse-to-fine multigrid algorithm for the finite-difference time-domain (FDTD) method is proposed and applied to calculate the time-harmonic electromagnetic field in the presence of a resonant structure. The obtained results and the related efficiency are compared to those ones achieved with a conventional merely fine-grid FDTD run. The method is easy to implement into an existing FDTD framework, and most efficient if the steady-state electromagnetic field at a given frequency has to be computed within the entire solution domain.Index Terms-Finite-difference time-domain (FDTD).

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Section: Numerical Resultsmentioning

confidence: 99%

“…These responses are then Fourier-transformed and yield at those chosen points the frequency-domain characteristics, which can be used to find out the resonant frequencies, the , etc. Alternatively, the resonance frequencies can be efficiently found by using a Systems-Identification method [3].…”

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confidence: 99%

A simple multigrid approach for calculating time-harmonic fields with FDTD

Klinkenbusch

2005

IEEE Microw. Wireless Compon. Lett.

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“…Being based on the leapfrog technique, it requires long computation time. Signal processing techniques have been suggested [9] to improve computation time. FIR neural network [10] can be trained with a short segment of FDTD data set as a predictor.…”

Section: Neural Network For Fdtd Methodsmentioning

confidence: 99%

An overview of neural network methods in computational electromagnetics

Mishra

2001

Int J RF and Microwave Comp Aid Eng

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Some of the applications of neural network techniques in different methods, in computational electromagnetics, are highlighted. Applications in finite element method for automated mesh generation are described. Extrapolation of the FDTD data using FIR filterbased neural network is illustrated. Use of the neural network method in spectral domain technique for determination of resonant frequency of a microstrip resonator and the input impedance of a microstrip antenna are shown. Furthermore, potential application areas are identified.

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“…Several researchers have extracted parameters of interest from scattered fields or circuit response using signal processing techniques such as Prony's method [5,6], pencil of functions [7][8][9][10][11], autoregressive moving average (ARMA) [12,13], estimation of signal parameters via rotational invariance techniques (ESPRIT) [14][15][16], multiple signal classification (MUSIC) [17], and the state space method (SSM) [18][19][20][21]. Applications include computation of complex natural resonances and eigenmodes [22][23][24][25][26][27][28], impulse response characterization of time-domain signatures [29][30][31][32][33][34], broadband equivalent circuit parameter extraction [35,36], identification of radar target's features [37][38][39][40], extraction of biomedical vital signs from UWB radar measurements [41][42][43], and location of buried targets using ground penetrating radar [44]. The basis behind such signal processing applications is that the EM field scattered by an object can be adequately represented as a sum of damped sinusoids, whose amplitude and phase are closely related to the physical parameters of interest.…”

Section: Introductionmentioning

confidence: 99%

State Space Modelling of Electromagnetic Responses --- A Practical Approach to Extract Parameters From Simulated or Measured Data

Naishadham¹

2021

PIER B

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As computing power and algorithmic advances have evolved rapidly in the recent past, it is now feasible to solve complex electromagnetic (EM) problems involving scattering, radar cross section, antenna design, microwave circuit design, artificial EM materials, etc., using full-wave numerical methods. Several general-purpose commercial software packages are routinely used in industry in all these domains for EM analysis or design. However, the task of processing large sets of data output from these design studies and analyses is generally beyond the realm of commercial software packages, and the designer spends many hours writing problem-specific computer programs to extract the desired performance parameters. Some examples where auxiliary processing is needed for the extraction of EM parameters of interest include determination of coupling coefficients or the unloaded quality factor of a dielectric resonator, de-embedding feed lines from antenna currents, removal of discontinuity effects, and the extraction of equivalent circuit models. The same considerations as simulated data apply to the parametric analysis of measured data in the presence of noise. This paper presents a versatile datadriven spectral model derived from a state-space system representation of the computed or measured EM fields, from which all the parameters of interest can be extracted. An attractive feature of the state space method is its ability to identify a small number of system transfer function poles uniquely associated with a specific scattering mechanism or modal response, thereby enabling its isolation from the total response for detailed study. For example, using SSM, specular reflection and creeping waves on a smooth convex surface can be analyzed, and the diffraction at the edges can be isolated from the composite RCS of a large body. The desired field parameter is extracted or estimated from synthetic or measured data using a linear system of a relatively small model order that characterizes the specific modal response of interest. Illustrative examples will be presented to demonstrate the usefulness of the proposed approach for parametric extraction.

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Microwave structure characterization by a combination of FDTD and system identification methods

Cited by 26 publications

References 7 publications

A simple multigrid approach for calculating time-harmonic fields with FDTD

A simple multigrid approach for calculating time-harmonic fields with FDTD

An overview of neural network methods in computational electromagnetics

State Space Modelling of Electromagnetic Responses --- A Practical Approach to Extract Parameters From Simulated or Measured Data

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