Parametric bootstrapping of array data with a Generative Adversarial Network
ID:33 View Protection:ATTENDEE Updated Time:2020-08-05 10:16:59 Hits:492 Oral Presentation

Start Time:2020-06-09 14:40(Asia/Shanghai)

Duration:20min

Session:R Regular Session » R08Multi-Channel Imaging

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Abstract
Since the number of independent array data snapshots is limited by the availability of real-world data, we propose a parametric bootstrap for resampling. The proposed parametric bootstrap is based on a generative adversarial network (GAN) following the generative approach to machine learning. For the GAN model we chose the Wasserstein GAN with penalized norm of gradient of the critic with respect to its input (wGAN\_gp). The approach is demonstrated with synthetic and real-world ocean acoustic array data.
Keywords
generative adversarial network; GAN; sample covariance matrix; DOA estimation; Hellinger distance
Speaker
Peter Gerstoft
University of California, San Diego, USA

Submission Author
Peter Gerstoft University of California, San Diego, USA
Herbert Groll TU Wien, Austria
Christoph F TU Wien, Austria
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Important Date
  • Conference Date

    Jun 08

    2020

    to

    Jun 11

    2020

  • Jan 12 2020

    Draft paper submission deadline

  • Apr 15 2020

    Early Bird Registration

Sponsored By
IEEE Signal Processing Society
Organized By
Zhejiang University
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