Performance analysis of deep neural networks for direction of arrival estimation of multiple sources
Abstract Recently, popular machine learning algorithms have successfully been applied to the direction of arrival (DOA) estimation. An implementation of determination of DOA estimation is presented based on deep neural networks (DNNs) to reduce the computational complexity of traditional superresolu...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Hindawi-IET
2023-03-01
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| Series: | IET Signal Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/sil2.12178 |
| _version_ | 1797426561037631488 |
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| author | Min Chen Xingpeng Mao Xiuhong Wang |
| author_facet | Min Chen Xingpeng Mao Xiuhong Wang |
| author_sort | Min Chen |
| collection | DOAJ |
| description | Abstract Recently, popular machine learning algorithms have successfully been applied to the direction of arrival (DOA) estimation. An implementation of determination of DOA estimation is presented based on deep neural networks (DNNs) to reduce the computational complexity of traditional superresolution DOA estimation methods. The classical DOA estimation algorithms have limitations due to unforeseen effects, such as array perturbations. Instead of computing an inverse mapping based on the incomplete forward mapping that relates the signal directions to the array outputs, the DOA problem is approached as a mapping, which can be approximated using a suitable DNN trained with input output pairs. The neural network architecture is based on a multilayer perception and a group of parallel DNNs to perform detection and DOA estimation, respectively. Simulation results are performed to investigate the effect of network parameters on estimation accuracy so that they can be roughly determined in the case of one signal scenario. Based on a set of simulations and experimental measurements, the performance of the optimum network is also assessed and compared to that of the classical DOA estimation methods for multiple signals. It has been shown that the proposed method can not only achieve reasonably high DOA estimation accuracy, but also dramatically reduce the computational complexity and the memory space. |
| first_indexed | 2024-03-09T08:31:59Z |
| format | Article |
| id | doaj.art-a5a4dd71a8324370a87af587fceab09a |
| institution | Directory Open Access Journal |
| issn | 1751-9675 1751-9683 |
| language | English |
| last_indexed | 2024-03-09T08:31:59Z |
| publishDate | 2023-03-01 |
| publisher | Hindawi-IET |
| record_format | Article |
| series | IET Signal Processing |
| spelling | doaj.art-a5a4dd71a8324370a87af587fceab09a2023-12-02T19:37:36ZengHindawi-IETIET Signal Processing1751-96751751-96832023-03-01173n/an/a10.1049/sil2.12178Performance analysis of deep neural networks for direction of arrival estimation of multiple sourcesMin Chen0Xingpeng Mao1Xiuhong Wang2School of Electronics and Information Engineering Harbin Institute of Technology Harbin ChinaSchool of Electronics and Information Engineering Harbin Institute of Technology Harbin ChinaSchool of Information and Electrical Engineering Harbin Institute of Technology (Weihai) Weihai ChinaAbstract Recently, popular machine learning algorithms have successfully been applied to the direction of arrival (DOA) estimation. An implementation of determination of DOA estimation is presented based on deep neural networks (DNNs) to reduce the computational complexity of traditional superresolution DOA estimation methods. The classical DOA estimation algorithms have limitations due to unforeseen effects, such as array perturbations. Instead of computing an inverse mapping based on the incomplete forward mapping that relates the signal directions to the array outputs, the DOA problem is approached as a mapping, which can be approximated using a suitable DNN trained with input output pairs. The neural network architecture is based on a multilayer perception and a group of parallel DNNs to perform detection and DOA estimation, respectively. Simulation results are performed to investigate the effect of network parameters on estimation accuracy so that they can be roughly determined in the case of one signal scenario. Based on a set of simulations and experimental measurements, the performance of the optimum network is also assessed and compared to that of the classical DOA estimation methods for multiple signals. It has been shown that the proposed method can not only achieve reasonably high DOA estimation accuracy, but also dramatically reduce the computational complexity and the memory space.https://doi.org/10.1049/sil2.12178direction‐of‐arrival estimationlearning (artificial intelligence)neural netsradar signal processingsignal processing |
| spellingShingle | Min Chen Xingpeng Mao Xiuhong Wang Performance analysis of deep neural networks for direction of arrival estimation of multiple sources IET Signal Processing direction‐of‐arrival estimation learning (artificial intelligence) neural nets radar signal processing signal processing |
| title | Performance analysis of deep neural networks for direction of arrival estimation of multiple sources |
| title_full | Performance analysis of deep neural networks for direction of arrival estimation of multiple sources |
| title_fullStr | Performance analysis of deep neural networks for direction of arrival estimation of multiple sources |
| title_full_unstemmed | Performance analysis of deep neural networks for direction of arrival estimation of multiple sources |
| title_short | Performance analysis of deep neural networks for direction of arrival estimation of multiple sources |
| title_sort | performance analysis of deep neural networks for direction of arrival estimation of multiple sources |
| topic | direction‐of‐arrival estimation learning (artificial intelligence) neural nets radar signal processing signal processing |
| url | https://doi.org/10.1049/sil2.12178 |
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