Sparse Bayesian multiple sources localization using variational approximation for Laplace priors-Reference-Cited by-同舟云学术

Sparse Bayesian multiple sources localization using variational approximation for Laplace priors

Published:2022-06 Issue: Volume:126 Page:103460
ISSN:1051-2004
Container-title:Digital Signal Processing
language:en
Short-container-title:Digital Signal Processing

Author:

Tang Roubing,Zhang Qiaoling^ORCID,Zhang Weiwei,Ma Han

Funder

National Natural Science Foundation of China

China Postdoctoral Science Foundation

Natural Science Foundation of Zhejiang Province

Key Technology Research and Development Program of Shandong

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Signal Processing,Artificial Intelligence,Applied Mathematics,Computer Vision and Pattern Recognition,Statistics, Probability and Uncertainty,Computational Theory and Mathematics

Reference46 articles.

1. Machine learning in wireless sensor networks: algorithms, strategies, and applications;Alsheikh;IEEE Commun. Surv. Tutor.,2014

2. Minimization of arc tangent function penalty for off-grid multi-source passive localization by using a moving array;Bao;Digit. Signal Process.,2021

3. Application of a sub–0.1-mm3 implantable mote for in vivo real-time wireless temperature sensing;Shi;Sci. Adv.,2021

4. Survey of radio propagation models for acoustic applications in underwater wireless sensor network;Saini;Int. J. Sensors Wireless Commun. Control,2021

5. Analysis of Water Contamination and Reporting Using Wireless Sensors Network, vol. 1964;Thouhid,2021

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