Results 61 to 70 of about 7,085,552 (171)

Sparse-Bayesian-learning-based translational motion estimation of electromagnetic vortex imaging

The Journal of Engineering, 2019
The electromagnetic (EM) vortex imaging has been found to have a great potential application prospect in the imaging radar field. However, current studies focus on the motionless target, which seriously limits its application in practice.
Rui Li, Zhi-qiang Ma, Qun Zhang, Ying Luo, Bi-shuai Liang, Guang-ming Li   +5 more
doaj   +1 more source

Entropy‐Greedy Node Selection Algorithm in Spectrum Map Construction

IET Communications, Volume 19, Issue 1, January/December 2025.
This study proposes an EGNS framework to address the sensor selection challenge in spectrum map construction. By combining rough spatial sector partitioning with a refined greedy algorithm maximizing information gain, EGNS achieves superior reconstruction accuracy and lower sensing costs compared to conventional sampling approaches, making it well ...
Ruoyu Mo, Jianzhao Zhang, Changhua Yao, Chengcheng Si   +3 more
wiley   +1 more source

Incremental Sparse Bayesian Learning for Parameter Estimation of Superimposed Signals [PDF]

, 2013
This work discuses a novel algorithm for joint sparse estimation of superimposed signals and their parameters. The proposed method is based on two concepts: a variational Bayesian version of the incremental sparse Bayesian learning (SBL)- fast ...
Wang, Wei, Thomas, Jost, Shutin, Dmitriy, Jost, Thomas   +3 more
core   +1 more source

Noise Integral‐Based Sparse Bayesian Learning for DOA Estimation Using Grid Pruning and Adaptation

Electronics Letters, Volume 61, Issue 1, January/December 2025.
This paper proposes a DOA estimation algorithm using grid pruning and adaptation based on noise integral‐based sparse Bayesian learning (AGNISBL) method. It addresses the traditional SBL method high complexity, grid mismatch and inappropriate initial values of noise problems.
Yinian Liang, Xuhui Zheng, Wenqi Meng, Jie Li, Fangjiong Chen   +4 more
wiley   +1 more source

Adaptive Sensory Updates and Disturbance Compensation for Predicting RUL Distribution in Nonlinear Degradation Systems

IEEE Access
Accurate remaining useful life (RUL) prediction for nonlinear degradation systems is essential for effective predictive maintenance. In previous studies, statistical methods have been widely used for RUL prediction.
Bac Viet Nguyen, Hieu van Le, Jae Wook Jeon   +2 more
doaj   +1 more source

Sparse Bayesian learning using hierarchical synthesis prior for STAP

IET Radar, Sonar &Navigation, Volume 19, Issue 1, January/December 2025.
In this paper, the authors propose a novel SBL algorithm using hierarchical synthesis prior and apply it to STAP technology to improve clutter suppression performance under limited sample conditions. In addition, in order to reduce the computational complexity, the authors propose a local space–time dictionary construction method.
Junxiang Cao, Tong Wang, Weichen Cui
wiley   +1 more source

Sparse Bayesian Learning-Based Target Imaging and Parameter Estimation for Monostatic MIMO Radar Systems

IEEE Access, 2018
This paper presents novel sparse Bayesian learning (SBL)-based target imaging and parameter estimation techniques in monostatic multiple-input multiple-output (MIMO) radar systems for practical scenarios with insufficient observation samples and unknown ...
Amrita Mishra, Vini Gupta, Saumya Dwivedi, Aditya K. Jagannatham, Pramod K. Varshney   +4 more
doaj   +1 more source

An Effective Method Incorporating Binary Prior Information for Programmable Metasurface‐Based Microwave Computational Imaging

IET Radar, Sonar &Navigation, Volume 19, Issue 1, January/December 2025.
In this work, a method incorporating a priori binary information is introduced into programmable metasurface‐based microwave computational imaging (PMS‐MCI) for recovering the shape of metallic objects. Specifically, the reconstruction problem of metallic objects is firstly linearised using the physical optical approximation.
Fang‐Fang Wang, Hai‐Fei Yang, Hang Zhao, Yang Bao, Yiqian Mao, Qing Huo Liu   +5 more
wiley   +1 more source

Sparse Bayesian Learning Using Adaptive LASSO Priors

, 2022
To improve the recovery accuracy of sparse signal, we study on sparse Bayesian learning (SBL) algorithm using adaptive least absolute shrinkage and selection operator (LASSO) priors.
Bai, Zong Long, Shi, Li Ming, Sun, Jin Wei   +2 more
core   +1 more source

Details on \u3ci\u3eCSA-SBL\u3c/i\u3e: An Algorithm for Sparse Bayesian Learning Boosted by Partial Erroneous Support Knowledge [PDF]

, 2019
This report provides details on CSA-SBL(VB) algorithm for the recovery of sparse signals with unknown clustering pattern. More specifically, we deal with the recovery of sparse signals with unknown clustering pattern in the case of having partial ...
Moon, Todd K., Shekaramiz, Mohammad, Gunther, Jacob H.   +2 more
core   +1 more source