Complex multitask compressive sensing using Laplace priors

Abstract Most existing Bayesian compressive sensing (BCS) algorithms are developed in real numbers. This results in many difficulties in applying BCS to solve complex‐valued problems. To overcome this limitation, this letter extends the existing real‐valued BCS framework to the complex‐valued BCS fr...

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Autores principales:	Qilei Zhang, Zhen Dong, Yongsheng Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/951be62ebbdc4752b8bb1213bbebddf8
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spelling	oai:doaj.org-article:951be62ebbdc4752b8bb1213bbebddf82021-12-03T08:34:31ZComplex multitask compressive sensing using Laplace priors1350-911X0013-519410.1049/ell2.12331https://doaj.org/article/951be62ebbdc4752b8bb1213bbebddf82021-12-01T00:00:00Zhttps://doi.org/10.1049/ell2.12331https://doaj.org/toc/0013-5194https://doaj.org/toc/1350-911XAbstract Most existing Bayesian compressive sensing (BCS) algorithms are developed in real numbers. This results in many difficulties in applying BCS to solve complex‐valued problems. To overcome this limitation, this letter extends the existing real‐valued BCS framework to the complex‐valued BCS framework. Within this framework, the multitask learning setting, where L tasks are statistically interrelated and share the same prior, is considered. It is verified by numerical examples that the developed complex multitask compressive sensing (CMCS) algorithm is more accurate and effective than the existing algorithms for the complex sparse signal reconstructionsQilei ZhangZhen DongYongsheng ZhangWileyarticleElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENElectronics Letters, Vol 57, Iss 25, Pp 998-1000 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Electrical engineering. Electronics. Nuclear engineering TK1-9971 Qilei Zhang Zhen Dong Yongsheng Zhang Complex multitask compressive sensing using Laplace priors
description	Abstract Most existing Bayesian compressive sensing (BCS) algorithms are developed in real numbers. This results in many difficulties in applying BCS to solve complex‐valued problems. To overcome this limitation, this letter extends the existing real‐valued BCS framework to the complex‐valued BCS framework. Within this framework, the multitask learning setting, where L tasks are statistically interrelated and share the same prior, is considered. It is verified by numerical examples that the developed complex multitask compressive sensing (CMCS) algorithm is more accurate and effective than the existing algorithms for the complex sparse signal reconstructions
format	article
author	Qilei Zhang Zhen Dong Yongsheng Zhang
author_facet	Qilei Zhang Zhen Dong Yongsheng Zhang
author_sort	Qilei Zhang
title	Complex multitask compressive sensing using Laplace priors
title_short	Complex multitask compressive sensing using Laplace priors
title_full	Complex multitask compressive sensing using Laplace priors
title_fullStr	Complex multitask compressive sensing using Laplace priors
title_full_unstemmed	Complex multitask compressive sensing using Laplace priors
title_sort	complex multitask compressive sensing using laplace priors
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/951be62ebbdc4752b8bb1213bbebddf8
work_keys_str_mv	AT qileizhang complexmultitaskcompressivesensingusinglaplacepriors AT zhendong complexmultitaskcompressivesensingusinglaplacepriors AT yongshengzhang complexmultitaskcompressivesensingusinglaplacepriors
_version_	1718373404786032640

Complex multitask compressive sensing using Laplace priors

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