Accelerating Broadband DOA Estimation: A Real-Valued and Coherent Sparse Bayesian Approach for 5G Sensing
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| Publicado en: | Electronics vol. 14, no. 16 (2025), p. 3174-3200 |
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| Autor principal: | |
| Otros Autores: | , , |
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MDPI AG
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| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 022 | |a 2079-9292 | ||
| 024 | 7 | |a 10.3390/electronics14163174 |2 doi | |
| 035 | |a 3244012808 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231458 |2 nlm | ||
| 100 | 1 | |a Tong, Xin | |
| 245 | 1 | |a Accelerating Broadband DOA Estimation: A Real-Valued and Coherent Sparse Bayesian Approach for 5G Sensing | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a For applications like smart cities and autonomous driving, high-precision direction-of-arrival (DOA) estimation for 5G broadband signals is essential. A primary obstacle for existing methods is the spatial incoherence caused by multi-frequency propagation. We present a sparse Bayesian learning (SBL) algorithm specifically designed to resolve this issue while also minimizing computational load. The algorithm synergistically combines three key components: first, a multiple-signal classification (MUSIC)-like focusing technique ensures a coherent sparse model; second, a real-valued transformation significantly cuts down on computational complexity; and third, an optimized variational Bayesian inference accelerates convergence via root-finding. Validation against MUSIC and rootSBL confirms our method’s marked superiority in low-SNR, limited-snapshot, and multipath conditions delivering both higher accuracy and faster convergence. This work, thus, contributes an effective and practical solution for real-time 5G DOA sensing. | |
| 610 | 4 | |a CNN | |
| 653 | |a Sparsity | ||
| 653 | |a Propagation | ||
| 653 | |a Accuracy | ||
| 653 | |a Convergence | ||
| 653 | |a Bayesian analysis | ||
| 653 | |a Real time | ||
| 653 | |a Neural networks | ||
| 653 | |a Signal processing | ||
| 653 | |a Signal classification | ||
| 653 | |a Incoherence | ||
| 653 | |a Broadband | ||
| 653 | |a Unmanned aerial vehicles | ||
| 653 | |a Algorithms | ||
| 653 | |a Methods | ||
| 653 | |a Direction of arrival | ||
| 653 | |a Machine learning | ||
| 653 | |a Statistical inference | ||
| 700 | 1 | |a Hu Yinzhe | |
| 700 | 1 | |a Deng Zhongliang | |
| 700 | 1 | |a Hu Enwen | |
| 773 | 0 | |t Electronics |g vol. 14, no. 16 (2025), p. 3174-3200 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3244012808/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3244012808/fulltextwithgraphics/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3244012808/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |