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| Main Authors: | , , , , , , , |
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| Format: | Recurso digital |
| Language: | |
| Published: |
Zenodo
2025
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| Subjects: | |
| Online Access: | https://doi.org/10.5281/zenodo.17284545 |
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Table of Contents:
- <p>Accurate and computationally efficient direction-ofarrival (DoA) estimation remains a fundamental requirement in wireless communications, radar, and array signal processing. This paper presents novel residual network, a deep learning architecture that incorporates ghost blocks and squeeze-andexcitation (SE) modules into the ResNet framework to address the trade-off between accuracy and complexity. The introduction of ghost blocks reduces parameters and floating-point operations by approximately 50%, while SE modules improve feature discrimination with negligible overhead. Experimental evaluations demonstrate that the proposed architecture achieves high estimation accuracy, with F1 scores reaching 96.80% in single-signal scenarios and 89.21% with five simultaneous signals, alongside mean absolute errors between 0.016◦ and 0.161◦. The proposed approach thus provides a balanced solution that combines precision with computational efficiency, supporting real-time deployment.</p>