Notice
This item was automatically migrated from a legacy system. It's data has not been checked and might not meet the quality criteria of the present system.
Record link: 
http://hdl.handle.net/20.500.12708/77059
-
Title: 
Parametric Bootstrapping of Array Data with A Generative Adversarial Network
-
Citation: 
Gerstoft, P., Groll, H., & Mecklenbräuker, C. (2020). Parametric Bootstrapping of Array Data with A Generative Adversarial Network. In 2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM). IEEE Xplore. https://doi.org/10.1109/sam48682.2020.9104371
-
Publisher DOI: 
10.1109/sam48682.2020.9104371
-
Publication Type: 
Inproceedings - Full-Paper Contribution
en
Authors: 
Gerstoft, Peter 
Groll, Herbert 
Mecklenbräuker, Christoph 
-
Organisational Unit: 
E389-02 - Forschungsbereich Wireless Communications 
-
Published in: 
2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM) 
-
Date (published): 
2020
-
Number of Pages: 
5
-
Publisher: 
IEEE Xplore, Hangzhou, China, China
-
Peer reviewed: 
Yes
-
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.
en
Research Areas: 
Sensor Systems: 50%
Telecommunication: 50%
-
Science Branch: 
Elektrotechnik, Elektronik, Informationstechnik
-
Appears in Collections:
Conference Paper

Show full item record

Page view(s)

133
checked on Dec 1, 2023

Google ScholarTM

Check