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

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書誌詳細
出版年:	Electronics vol. 14, no. 16 (2025), p. 3174-3200
第一著者:	Tong, Xin
その他の著者:	Hu Yinzhe, Deng Zhongliang, Hu Enwen
出版事項:	MDPI AG
主題:	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
オンライン･アクセス:	Citation/Abstract Full Text + Graphics Full Text - PDF
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その他の書誌記述
抄録:	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
ソース:	Advanced Technologies & Aerospace Database