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Active learning applied to RCS computations with nonuniform sampling using different objective functions

Publication ,  Journal Article
Zhao, Z; Nie, Z; Carin, L
Published in: IEEE Transactions on Antennas and Propagation
April 1, 2007

An active learning framework is introduced to reduce the number of frequencies and angles one must consider for wideband monostatic scattering computations or measurements. This method is used to optimally select those frequencies and angles that would be most informative, resulting in nonuniform sampling and often a reduced number of points (vis-à-vis uniform sampling). In this paper we focus on jointly two-dimensional optimal sampling in frequency and incident angle θ for monostatic scattering. The method consists of two basic steps. One step involves estimation of model parameters using a least-square (LS) algorithm. The next step is to optimally choose the next point (frequency and θ) for analysis by the computational model or experiment. This new point is selected with the goal of reducing uncertainty in the parametric model (quantified via the Fisher information matrix). Iterating these two steps, a sequence of numerical computations or measurements are performed, each at the most informative point for learning the parameters of the associated simpler parametric model. This idea is demonstrated here in the context of reducing the number of points (frequencies and orientations) at which a computational model must be employed. And in order to avoid repeatedly gathering samplings at the edge of the input space, an alternative objective function is applied which makes the actively selected points closer to a region of interest. © 2007 IEEE.

Duke Scholars

Published In

IEEE Transactions on Antennas and Propagation

DOI

ISSN

0018-926X

Publication Date

April 1, 2007

Volume

55

Issue

4

Start / End Page

1214 / 1217

Related Subject Headings

  • Networking & Telecommunications
  • 4009 Electronics, sensors and digital hardware
  • 4008 Electrical engineering
  • 4006 Communications engineering
  • 1005 Communications Technologies
  • 0906 Electrical and Electronic Engineering
 

Citation

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Zhao, Z., Nie, Z., & Carin, L. (2007). Active learning applied to RCS computations with nonuniform sampling using different objective functions. IEEE Transactions on Antennas and Propagation, 55(4), 1214–1217. https://doi.org/10.1109/TAP.2007.893410
Zhao, Z., Z. Nie, and L. Carin. “Active learning applied to RCS computations with nonuniform sampling using different objective functions.” IEEE Transactions on Antennas and Propagation 55, no. 4 (April 1, 2007): 1214–17. https://doi.org/10.1109/TAP.2007.893410.
Zhao Z, Nie Z, Carin L. Active learning applied to RCS computations with nonuniform sampling using different objective functions. IEEE Transactions on Antennas and Propagation. 2007 Apr 1;55(4):1214–7.
Zhao, Z., et al. “Active learning applied to RCS computations with nonuniform sampling using different objective functions.” IEEE Transactions on Antennas and Propagation, vol. 55, no. 4, Apr. 2007, pp. 1214–17. Scopus, doi:10.1109/TAP.2007.893410.
Zhao Z, Nie Z, Carin L. Active learning applied to RCS computations with nonuniform sampling using different objective functions. IEEE Transactions on Antennas and Propagation. 2007 Apr 1;55(4):1214–1217.

Published In

IEEE Transactions on Antennas and Propagation

DOI

ISSN

0018-926X

Publication Date

April 1, 2007

Volume

55

Issue

4

Start / End Page

1214 / 1217

Related Subject Headings

  • Networking & Telecommunications
  • 4009 Electronics, sensors and digital hardware
  • 4008 Electrical engineering
  • 4006 Communications engineering
  • 1005 Communications Technologies
  • 0906 Electrical and Electronic Engineering