Cargando…
Multi-DOA estimation based on the KR image tensor and improved estimation network
Deep neural networks have shown great performance for direction-of-arrival (DOA) estimation problem, but it is necessary to design some suitable networks to solve the multi-DOA estimation problem. In this paper, we use Khatri–Rao product to increase the degree of freedom of antenna array and obtain...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7973726/ https://www.ncbi.nlm.nih.gov/pubmed/33737715 http://dx.doi.org/10.1038/s41598-021-85864-5 |
_version_ | 1783666881453883392 |
---|---|
author | Yuan, Ye Wu, Shuang Yang, Yong Yuan, Naichang |
author_facet | Yuan, Ye Wu, Shuang Yang, Yong Yuan, Naichang |
author_sort | Yuan, Ye |
collection | PubMed |
description | Deep neural networks have shown great performance for direction-of-arrival (DOA) estimation problem, but it is necessary to design some suitable networks to solve the multi-DOA estimation problem. In this paper, we use Khatri–Rao product to increase the degree of freedom of antenna array and obtain the image tensor of covariance matrix, then we propose an improved estimation network to process the tensor. We use the curriculum learning scheme and partial label strategy to develop a CurriculumNet training scheme. The training/validation results shows that the proposed training scheme can increase the generalization of the estimation network and improve the accuracy of network around [Formula: see text] . The estimation performance of the proposed network shows high-resolution results, which can distinguish two adjacent signals with angle difference of [Formula: see text] . Moreover, the proposed estimation network has root mean square estimation error lower than [Formula: see text] when signal noise ratio equals [Formula: see text] and can estimate DOAs precisely by only 8 snapshots, which performs much better than prior deep neural network based estimation methods and can estimate multi-DOA results under hostile estimation environments. |
format | Online Article Text |
id | pubmed-7973726 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-79737262021-03-19 Multi-DOA estimation based on the KR image tensor and improved estimation network Yuan, Ye Wu, Shuang Yang, Yong Yuan, Naichang Sci Rep Article Deep neural networks have shown great performance for direction-of-arrival (DOA) estimation problem, but it is necessary to design some suitable networks to solve the multi-DOA estimation problem. In this paper, we use Khatri–Rao product to increase the degree of freedom of antenna array and obtain the image tensor of covariance matrix, then we propose an improved estimation network to process the tensor. We use the curriculum learning scheme and partial label strategy to develop a CurriculumNet training scheme. The training/validation results shows that the proposed training scheme can increase the generalization of the estimation network and improve the accuracy of network around [Formula: see text] . The estimation performance of the proposed network shows high-resolution results, which can distinguish two adjacent signals with angle difference of [Formula: see text] . Moreover, the proposed estimation network has root mean square estimation error lower than [Formula: see text] when signal noise ratio equals [Formula: see text] and can estimate DOAs precisely by only 8 snapshots, which performs much better than prior deep neural network based estimation methods and can estimate multi-DOA results under hostile estimation environments. Nature Publishing Group UK 2021-03-18 /pmc/articles/PMC7973726/ /pubmed/33737715 http://dx.doi.org/10.1038/s41598-021-85864-5 Text en © The Author(s) 2021 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 Yuan, Ye Wu, Shuang Yang, Yong Yuan, Naichang Multi-DOA estimation based on the KR image tensor and improved estimation network |
title | Multi-DOA estimation based on the KR image tensor and improved estimation network |
title_full | Multi-DOA estimation based on the KR image tensor and improved estimation network |
title_fullStr | Multi-DOA estimation based on the KR image tensor and improved estimation network |
title_full_unstemmed | Multi-DOA estimation based on the KR image tensor and improved estimation network |
title_short | Multi-DOA estimation based on the KR image tensor and improved estimation network |
title_sort | multi-doa estimation based on the kr image tensor and improved estimation network |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7973726/ https://www.ncbi.nlm.nih.gov/pubmed/33737715 http://dx.doi.org/10.1038/s41598-021-85864-5 |
work_keys_str_mv | AT yuanye multidoaestimationbasedonthekrimagetensorandimprovedestimationnetwork AT wushuang multidoaestimationbasedonthekrimagetensorandimprovedestimationnetwork AT yangyong multidoaestimationbasedonthekrimagetensorandimprovedestimationnetwork AT yuannaichang multidoaestimationbasedonthekrimagetensorandimprovedestimationnetwork |