Robust Low-Snapshot DOA Estimation for Sparse Arrays via a Hybrid Convolutional Graph Neural Network
We propose a hybrid Convolutional Graph Neural Network (C-GNN) for direction-of-arrival (DOA) estimation in sparse sensor arrays under low-snapshot conditions. The C-GNN architecture combines 1D convolutional layers for local spatial feature extraction with graph convolutional layers for global stru...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/15/4563 |
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| author | Hongliang Zhu Hongxi Zhao Chunshan Bao Yiran Shi Wenchao He |
| author_facet | Hongliang Zhu Hongxi Zhao Chunshan Bao Yiran Shi Wenchao He |
| author_sort | Hongliang Zhu |
| collection | DOAJ |
| description | We propose a hybrid Convolutional Graph Neural Network (C-GNN) for direction-of-arrival (DOA) estimation in sparse sensor arrays under low-snapshot conditions. The C-GNN architecture combines 1D convolutional layers for local spatial feature extraction with graph convolutional layers for global structural learning, effectively capturing both fine-grained and long-range array dependencies. Leveraging the difference coarray technique, the sparse array is transformed into a virtual uniform linear array (VULA) to enrich the spatial sampling; real-valued covariance matrices derived from the array measurements are used as the network’s input features. A final multi-layer perceptron (MLP) regression module then maps the learned representations to continuous DOA angle estimates. This approach capitalizes on the increased degrees of freedom offered by the virtual array while inherently incorporating the array’s geometric relationships via graph-based learning. The proposed C-GNN demonstrates robust performance in noisy, low-data scenarios, reliably estimating source angles even with very limited snapshots. By focusing on methodological innovation rather than bespoke architectural tuning, the framework shows promise for data-efficient DOA estimation in challenging practical conditions. |
| format | Article |
| id | doaj-art-1c501997ca754ac8b34cf18bf1b18f5a |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-1c501997ca754ac8b34cf18bf1b18f5a2025-08-20T03:02:56ZengMDPI AGSensors1424-82202025-07-012515456310.3390/s25154563Robust Low-Snapshot DOA Estimation for Sparse Arrays via a Hybrid Convolutional Graph Neural NetworkHongliang Zhu0Hongxi Zhao1Chunshan Bao2Yiran Shi3Wenchao He4College of Communications Engineering, Jilin University, Changchun 130015, ChinaCollege of Communications Engineering, Jilin University, Changchun 130015, ChinaCollege of Communications Engineering, Jilin University, Changchun 130015, ChinaCollege of Communications Engineering, Jilin University, Changchun 130015, ChinaCollege of Communications Engineering, Jilin University, Changchun 130015, ChinaWe propose a hybrid Convolutional Graph Neural Network (C-GNN) for direction-of-arrival (DOA) estimation in sparse sensor arrays under low-snapshot conditions. The C-GNN architecture combines 1D convolutional layers for local spatial feature extraction with graph convolutional layers for global structural learning, effectively capturing both fine-grained and long-range array dependencies. Leveraging the difference coarray technique, the sparse array is transformed into a virtual uniform linear array (VULA) to enrich the spatial sampling; real-valued covariance matrices derived from the array measurements are used as the network’s input features. A final multi-layer perceptron (MLP) regression module then maps the learned representations to continuous DOA angle estimates. This approach capitalizes on the increased degrees of freedom offered by the virtual array while inherently incorporating the array’s geometric relationships via graph-based learning. The proposed C-GNN demonstrates robust performance in noisy, low-data scenarios, reliably estimating source angles even with very limited snapshots. By focusing on methodological innovation rather than bespoke architectural tuning, the framework shows promise for data-efficient DOA estimation in challenging practical conditions.https://www.mdpi.com/1424-8220/25/15/4563direction-of-arrival (DOA) estimationgraph neural networklow snapshotsarray signal processing |
| spellingShingle | Hongliang Zhu Hongxi Zhao Chunshan Bao Yiran Shi Wenchao He Robust Low-Snapshot DOA Estimation for Sparse Arrays via a Hybrid Convolutional Graph Neural Network Sensors direction-of-arrival (DOA) estimation graph neural network low snapshots array signal processing |
| title | Robust Low-Snapshot DOA Estimation for Sparse Arrays via a Hybrid Convolutional Graph Neural Network |
| title_full | Robust Low-Snapshot DOA Estimation for Sparse Arrays via a Hybrid Convolutional Graph Neural Network |
| title_fullStr | Robust Low-Snapshot DOA Estimation for Sparse Arrays via a Hybrid Convolutional Graph Neural Network |
| title_full_unstemmed | Robust Low-Snapshot DOA Estimation for Sparse Arrays via a Hybrid Convolutional Graph Neural Network |
| title_short | Robust Low-Snapshot DOA Estimation for Sparse Arrays via a Hybrid Convolutional Graph Neural Network |
| title_sort | robust low snapshot doa estimation for sparse arrays via a hybrid convolutional graph neural network |
| topic | direction-of-arrival (DOA) estimation graph neural network low snapshots array signal processing |
| url | https://www.mdpi.com/1424-8220/25/15/4563 |
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