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Super resolution DOA estimation based on deep neural network
Recently, deep neural network (DNN) studies on direction-of-arrival (DOA) estimations have attracted more and more attention. This new method gives an alternative way to deal with DOA problem and has successfully shown its potential application. However, these works are often restricted to previousl...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group UK
2020
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7670414/ https://www.ncbi.nlm.nih.gov/pubmed/33199771 http://dx.doi.org/10.1038/s41598-020-76608-y |
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author | Liu, Wanli |
author_facet | Liu, Wanli |
author_sort | Liu, Wanli |
collection | PubMed |
description | Recently, deep neural network (DNN) studies on direction-of-arrival (DOA) estimations have attracted more and more attention. This new method gives an alternative way to deal with DOA problem and has successfully shown its potential application. However, these works are often restricted to previously known signal number, same signal-to-noise ratio (SNR) or large intersignal angular distance, which will hinder their generalization in real application. In this paper, we present a novel DNN framework that realizes higher resolution and better generalization to random signal number and SNR. Simulation results outperform that of previous works and reach the state of the art. |
format | Online Article Text |
id | pubmed-7670414 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-76704142020-11-18 Super resolution DOA estimation based on deep neural network Liu, Wanli Sci Rep Article Recently, deep neural network (DNN) studies on direction-of-arrival (DOA) estimations have attracted more and more attention. This new method gives an alternative way to deal with DOA problem and has successfully shown its potential application. However, these works are often restricted to previously known signal number, same signal-to-noise ratio (SNR) or large intersignal angular distance, which will hinder their generalization in real application. In this paper, we present a novel DNN framework that realizes higher resolution and better generalization to random signal number and SNR. Simulation results outperform that of previous works and reach the state of the art. Nature Publishing Group UK 2020-11-16 /pmc/articles/PMC7670414/ /pubmed/33199771 http://dx.doi.org/10.1038/s41598-020-76608-y Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Liu, Wanli Super resolution DOA estimation based on deep neural network |
title | Super resolution DOA estimation based on deep neural network |
title_full | Super resolution DOA estimation based on deep neural network |
title_fullStr | Super resolution DOA estimation based on deep neural network |
title_full_unstemmed | Super resolution DOA estimation based on deep neural network |
title_short | Super resolution DOA estimation based on deep neural network |
title_sort | super resolution doa estimation based on deep neural network |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7670414/ https://www.ncbi.nlm.nih.gov/pubmed/33199771 http://dx.doi.org/10.1038/s41598-020-76608-y |
work_keys_str_mv | AT liuwanli superresolutiondoaestimationbasedondeepneuralnetwork |