Imagen de Portada

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

Guardado en:
Publicado en:Electronics vol. 14, no. 16 (2025), p. 3174-3200
Autor principal: Tong, Xin
Otros Autores: Hu Yinzhe, Deng Zhongliang, Hu Enwen
Publicado:
MDPI AG
Materias:
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
Acceso en línea:Citation/Abstract
Full Text + Graphics
Full Text - PDF
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
Resumen: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
Fuente:Advanced Technologies & Aerospace Database