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)


Session:R Regular Session » R08Multi-Channel Imaging

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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.
generative adversarial network; GAN; sample covariance matrix; DOA estimation; Hellinger distance
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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    Jun 11


  • Jan 12 2020

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  • Apr 15 2020

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IEEE Signal Processing Society
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Zhejiang University
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