Image de couverture de livre

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

Enregistré dans:
Publié dans:Electronics vol. 14, no. 16 (2025), p. 3174-3200
Auteur principal: Tong, Xin
Autres auteurs: Hu Yinzhe, Deng Zhongliang, Hu Enwen
Publié:
MDPI AG
Sujets:
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
Accès en ligne:Citation/Abstract
Full Text + Graphics
Full Text - PDF
Tags: Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
Résumé: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
Source:Advanced Technologies & Aerospace Database