An automated multi-layer perceptron discriminative neural network based on Bayesian optimization achieves high-precision one-source single-snapshot direction-of-arrival estimation
Abstract This paper proposes an innovative global solution which is a pioneering work applying automated machine learning algorithms to remarkable precision sparse underwater direction-of-arrival (DOA) estimation that views the subaquatic sparse-sampling DOA estimation problem as a classification pr...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-05-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-60798-w |
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| _version_ | 1846165372894445568 |
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| author | Bin Zhang Jiawen He Peishun Liu Liang Wang Ruichun Tang |
| author_facet | Bin Zhang Jiawen He Peishun Liu Liang Wang Ruichun Tang |
| author_sort | Bin Zhang |
| collection | DOAJ |
| description | Abstract This paper proposes an innovative global solution which is a pioneering work applying automated machine learning algorithms to remarkable precision sparse underwater direction-of-arrival (DOA) estimation that views the subaquatic sparse-sampling DOA estimation problem as a classification prediction task. The proposed solution, termed automated multi-layer perceptron discriminative neural network (AutoMPDNN), is built upon a Bayesian optimization framework. AutoMPDNN transforms sparsely sampled time-domain signals into the complex domain, preserving essential components in a one-source single-snapshot scenario. Leveraging Bayesian optimization principles, the algorithm embeds necessary hyperparameters into the loss function, effectively defining it as a maximum likelihood problem using the upper confidence bound function and incorporating sparse signal features. We also explore the model space architecture and introduce variants of AutoMPDNN, denoted as AutoMPDNNs_ln (n = 2,3,4). Through a series of plane wave simulation experiments, it is demonstrated that AutoMPDNN achieves the highest prediction performance for one-source single-snapshot scenarios compared to classical DOA estimation algorithms that incorporate sparse representation approaches, as well as contemporary deep learning DOA methods under varying conditions. |
| format | Article |
| id | doaj-art-f9429d6f32b54cf5aa4ebc5109500b23 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-f9429d6f32b54cf5aa4ebc5109500b232024-11-17T12:20:31ZengNature PortfolioScientific Reports2045-23222024-05-0114111410.1038/s41598-024-60798-wAn automated multi-layer perceptron discriminative neural network based on Bayesian optimization achieves high-precision one-source single-snapshot direction-of-arrival estimationBin Zhang0Jiawen He1Peishun Liu2Liang Wang3Ruichun Tang4Department of Computer Science and Technology, Ocean University of ChinaDepartment of Computer Science and Technology, Ocean University of ChinaDepartment of Computer Science and Technology, Ocean University of ChinaDepartment of Marine Technology, Ocean University of ChinaDepartment of Computer Science and Technology, Ocean University of ChinaAbstract This paper proposes an innovative global solution which is a pioneering work applying automated machine learning algorithms to remarkable precision sparse underwater direction-of-arrival (DOA) estimation that views the subaquatic sparse-sampling DOA estimation problem as a classification prediction task. The proposed solution, termed automated multi-layer perceptron discriminative neural network (AutoMPDNN), is built upon a Bayesian optimization framework. AutoMPDNN transforms sparsely sampled time-domain signals into the complex domain, preserving essential components in a one-source single-snapshot scenario. Leveraging Bayesian optimization principles, the algorithm embeds necessary hyperparameters into the loss function, effectively defining it as a maximum likelihood problem using the upper confidence bound function and incorporating sparse signal features. We also explore the model space architecture and introduce variants of AutoMPDNN, denoted as AutoMPDNNs_ln (n = 2,3,4). Through a series of plane wave simulation experiments, it is demonstrated that AutoMPDNN achieves the highest prediction performance for one-source single-snapshot scenarios compared to classical DOA estimation algorithms that incorporate sparse representation approaches, as well as contemporary deep learning DOA methods under varying conditions.https://doi.org/10.1038/s41598-024-60798-w |
| spellingShingle | Bin Zhang Jiawen He Peishun Liu Liang Wang Ruichun Tang An automated multi-layer perceptron discriminative neural network based on Bayesian optimization achieves high-precision one-source single-snapshot direction-of-arrival estimation Scientific Reports |
| title | An automated multi-layer perceptron discriminative neural network based on Bayesian optimization achieves high-precision one-source single-snapshot direction-of-arrival estimation |
| title_full | An automated multi-layer perceptron discriminative neural network based on Bayesian optimization achieves high-precision one-source single-snapshot direction-of-arrival estimation |
| title_fullStr | An automated multi-layer perceptron discriminative neural network based on Bayesian optimization achieves high-precision one-source single-snapshot direction-of-arrival estimation |
| title_full_unstemmed | An automated multi-layer perceptron discriminative neural network based on Bayesian optimization achieves high-precision one-source single-snapshot direction-of-arrival estimation |
| title_short | An automated multi-layer perceptron discriminative neural network based on Bayesian optimization achieves high-precision one-source single-snapshot direction-of-arrival estimation |
| title_sort | automated multi layer perceptron discriminative neural network based on bayesian optimization achieves high precision one source single snapshot direction of arrival estimation |
| url | https://doi.org/10.1038/s41598-024-60798-w |
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