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Accelerating Broadband DOA Estimation: A Real-Valued and Coherent Sparse Bayesian Approach for 5G Sensing

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Publicado no:Electronics vol. 14, no. 16 (2025), p. 3174-3200
Autor principal: Tong, Xin
Outros Autores: Hu Yinzhe, Deng Zhongliang, Hu Enwen
Publicado em:
MDPI AG
Assuntos:
CNN
Sparsity
Propagation
Accuracy
Convergence
Bayesian analysis
Real time
Neural networks
Signal processing
Signal classification
Incoherence
Broadband
Unmanned aerial vehicles
Algorithms
Methods
Direction of arrival
Machine learning
Statistical inference
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Resumo: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.
ISSN:2079-9292
DOI:10.3390/electronics14163174
Fonte:Advanced Technologies & Aerospace Database