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Centroid Optimization of DNN Classification in DOA Estimation for UAV
Classifications based on deep learning have been widely applied in the estimation of the direction of arrival (DOA) of signal. Due to the limited number of classes, the classification of DOA cannot satisfy the required prediction accuracy of signals from random azimuth in real applications. This pap...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10007322/ https://www.ncbi.nlm.nih.gov/pubmed/36904717 http://dx.doi.org/10.3390/s23052513 |
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author | Wu, Long Zhang, Zidan Yang, Xu Xu, Lu Chen, Shuyu Zhang, Yong Zhang, Jianlong |
author_facet | Wu, Long Zhang, Zidan Yang, Xu Xu, Lu Chen, Shuyu Zhang, Yong Zhang, Jianlong |
author_sort | Wu, Long |
collection | PubMed |
description | Classifications based on deep learning have been widely applied in the estimation of the direction of arrival (DOA) of signal. Due to the limited number of classes, the classification of DOA cannot satisfy the required prediction accuracy of signals from random azimuth in real applications. This paper presents a Centroid Optimization of deep neural network classification (CO-DNNC) to improve the estimation accuracy of DOA. CO-DNNC includes signal preprocessing, classification network, and Centroid Optimization. The DNN classification network adopts a convolutional neural network, including convolutional layers and fully connected layers. The Centroid Optimization takes the classified labels as the coordinates and calculates the azimuth of received signal according to the probabilities of the Softmax output. The experimental results show that CO-DNNC is capable of acquiring precise and accurate estimation of DOA, especially in the cases of low SNRs. In addition, CO-DNNC requires lower numbers of classes under the same condition of prediction accuracy and SNR, which reduces the complexity of the DNN network and saves training and processing time. |
format | Online Article Text |
id | pubmed-10007322 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-100073222023-03-12 Centroid Optimization of DNN Classification in DOA Estimation for UAV Wu, Long Zhang, Zidan Yang, Xu Xu, Lu Chen, Shuyu Zhang, Yong Zhang, Jianlong Sensors (Basel) Article Classifications based on deep learning have been widely applied in the estimation of the direction of arrival (DOA) of signal. Due to the limited number of classes, the classification of DOA cannot satisfy the required prediction accuracy of signals from random azimuth in real applications. This paper presents a Centroid Optimization of deep neural network classification (CO-DNNC) to improve the estimation accuracy of DOA. CO-DNNC includes signal preprocessing, classification network, and Centroid Optimization. The DNN classification network adopts a convolutional neural network, including convolutional layers and fully connected layers. The Centroid Optimization takes the classified labels as the coordinates and calculates the azimuth of received signal according to the probabilities of the Softmax output. The experimental results show that CO-DNNC is capable of acquiring precise and accurate estimation of DOA, especially in the cases of low SNRs. In addition, CO-DNNC requires lower numbers of classes under the same condition of prediction accuracy and SNR, which reduces the complexity of the DNN network and saves training and processing time. MDPI 2023-02-24 /pmc/articles/PMC10007322/ /pubmed/36904717 http://dx.doi.org/10.3390/s23052513 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Wu, Long Zhang, Zidan Yang, Xu Xu, Lu Chen, Shuyu Zhang, Yong Zhang, Jianlong Centroid Optimization of DNN Classification in DOA Estimation for UAV |
title | Centroid Optimization of DNN Classification in DOA Estimation for UAV |
title_full | Centroid Optimization of DNN Classification in DOA Estimation for UAV |
title_fullStr | Centroid Optimization of DNN Classification in DOA Estimation for UAV |
title_full_unstemmed | Centroid Optimization of DNN Classification in DOA Estimation for UAV |
title_short | Centroid Optimization of DNN Classification in DOA Estimation for UAV |
title_sort | centroid optimization of dnn classification in doa estimation for uav |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10007322/ https://www.ncbi.nlm.nih.gov/pubmed/36904717 http://dx.doi.org/10.3390/s23052513 |
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