Complex multitask Bayesian compressive sensing algorithm using modified Laplace priors

被引：0
|
作者：
Zhang Q. [1 ]
Sun B. [2 ]
机构：
[1] College of Electronic Science and Technology, National University of Defense Technology, Changsha
[2] Beijing Institute of Tracking and Telecommunication Technology, Beijing
来源：
Guofang Keji Daxue Xuebao/Journal of National University of Defense Technology | 2023年 / 45卷 / 05期
关键词：
Bayesian compressive sensing; complex Bayesian compressive sensing; modified Laplace priors; multitask learning;
D O I：
10.11887/j.cn.202305017
中图分类号：
学科分类号：
摘要：
To extend the existing real-valued BCS (Bayesian compressive sensing) framework to the complex-valued one, a CMBCS-MLP (complex multitask Bayesian compressive sensing algorithm using modified Laplace priors) was developed to eliminate the impact of measurement noise variance, and a fast algorithm based on sequential operations was further derived. It is demonstrated by numerical examples that the developed CMBCS-MLP algorithm is more accurate and robust than the existing algorithms in the complex sparse signal reconstructions. © 2023 National University of Defense Technology. All rights reserved.
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页码：150 / 156
页数：6
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