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Stochastic analysis of the impact of substrate compression on the performance of textile antennas

Marco Rossi (UGent) , Sam Agneessens (UGent) , Hendrik Rogier (UGent) and Dries Vande Ginste (UGent)
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Abstract
One of the many adverse effects modifying the performance of textile antennas in real operating conditions is substrate compression. Therefore, this communication presents a stochastic collocation method (SCM) that either relies on the generalized polynomial chaos (gPC) expansion or on a novel Hermite-Pade approximant. The method is introduced to rigorously quantify the effect of random variations in the height and the permittivity of the substrate on the figures of merit of a textile antenna. Next, the joint height and permittivity probability distribution of a compressible substrate are characterized by means of a new measurement setup based on a resonant-perturbation technique. Finally, the method is validated for a probe-fed GPS textile antenna. It is shown that Hermite-Pade approximants model the highly nonlinear relationship between these substrate random variables and the figures of merit of the antenna more efficiently than the gPC. Moreover, a Kolmogorov-Smirnoff test proves that the resulting distributions of the antenna's figures of merit are as accurate as those obtained by means of aMonte-Carlo (MC) analysis, with demonstrated speedup factors up to 123.
Keywords
VARIABILITY ANALYSIS, IBCN, POLYNOMIAL-CHAOS, UNCERTAINTY, FREQUENCY, SYSTEMS, Pade approximation, polynomial chaos, stochastic collocation, substrate compression, textile antenna, variability analysis

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MLA
Rossi, Marco, et al. “Stochastic Analysis of the Impact of Substrate Compression on the Performance of Textile Antennas.” IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, vol. 64, no. 6, 2016, pp. 2507–12, doi:10.1109/TAP.2016.2543780.
APA
Rossi, M., Agneessens, S., Rogier, H., & Vande Ginste, D. (2016). Stochastic analysis of the impact of substrate compression on the performance of textile antennas. IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, 64(6), 2507–2512. https://doi.org/10.1109/TAP.2016.2543780
Chicago author-date
Rossi, Marco, Sam Agneessens, Hendrik Rogier, and Dries Vande Ginste. 2016. “Stochastic Analysis of the Impact of Substrate Compression on the Performance of Textile Antennas.” IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 64 (6): 2507–12. https://doi.org/10.1109/TAP.2016.2543780.
Chicago author-date (all authors)
Rossi, Marco, Sam Agneessens, Hendrik Rogier, and Dries Vande Ginste. 2016. “Stochastic Analysis of the Impact of Substrate Compression on the Performance of Textile Antennas.” IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 64 (6): 2507–2512. doi:10.1109/TAP.2016.2543780.
Vancouver
1.
Rossi M, Agneessens S, Rogier H, Vande Ginste D. Stochastic analysis of the impact of substrate compression on the performance of textile antennas. IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION. 2016;64(6):2507–12.
IEEE
[1]
M. Rossi, S. Agneessens, H. Rogier, and D. Vande Ginste, “Stochastic analysis of the impact of substrate compression on the performance of textile antennas,” IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, vol. 64, no. 6, pp. 2507–2512, 2016.
@article{8130657,
  abstract     = {{One of the many adverse effects modifying the performance of textile antennas in real operating conditions is substrate compression. Therefore, this communication presents a stochastic collocation method (SCM) that either relies on the generalized polynomial chaos (gPC) expansion or on a novel Hermite-Pade approximant. The method is introduced to rigorously quantify the effect of random variations in the height and the permittivity of the substrate on the figures of merit of a textile antenna. Next, the joint height and permittivity probability distribution of a compressible substrate are characterized by means of a new measurement setup based on a resonant-perturbation technique. Finally, the method is validated for a probe-fed GPS textile antenna. It is shown that Hermite-Pade approximants model the highly nonlinear relationship between these substrate random variables and the figures of merit of the antenna more efficiently than the gPC. Moreover, a Kolmogorov-Smirnoff test proves that the resulting distributions of the antenna's figures of merit are as accurate as those obtained by means of aMonte-Carlo (MC) analysis, with demonstrated speedup factors up to 123.}},
  author       = {{Rossi, Marco and Agneessens, Sam and Rogier, Hendrik and Vande Ginste, Dries}},
  issn         = {{0018-926X}},
  journal      = {{IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION}},
  keywords     = {{VARIABILITY ANALYSIS,IBCN,POLYNOMIAL-CHAOS,UNCERTAINTY,FREQUENCY,SYSTEMS,Pade approximation,polynomial chaos,stochastic collocation,substrate compression,textile antenna,variability analysis}},
  language     = {{eng}},
  number       = {{6}},
  pages        = {{2507--2512}},
  title        = {{Stochastic analysis of the impact of substrate compression on the performance of textile antennas}},
  url          = {{http://doi.org/10.1109/TAP.2016.2543780}},
  volume       = {{64}},
  year         = {{2016}},
}

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