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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국정보처리학회 JIPS(Journal of Information Processing Systems) JIPS(Journal of Information Processing Systems) 제15권 제2호
- 발행연도
- 2019.1
- 수록면
- 410 - 421 (12page)
이용수
초록· 키워드
오류제보하기
Distributed compressed sensing (DCS) states that we can recover the sparse signals from very few linearmeasurements. Various studies about DCS have been carried out recently. In many practical applications, thereis no prior information except for standard sparsity on signals. The typical example is the sparse signals haveblock-sparse structures whose non-zero coefficients occurring in clusters, while the cluster pattern is usuallyunavailable as the prior information. To discuss this issue, a new algorithm, called backtracking-based adaptiveorthogonal matching pursuit for block distributed compressed sensing (DCSBBAOMP), is proposed. Incontrast to existing block methods which consider the single-channel signal reconstruction, the DCSBBAOMPresorts to the multi-channel signals reconstruction. Moreover, this algorithm is an iterative approach, whichconsists of forward selection and backward removal stages in each iteration. An advantage of this method isthat perfect reconstruction performance can be achieved without prior information on the block-sparsitystructure. Numerical experiments are provided to illustrate the desirable performance of the proposed method.
목차
등록된 정보가 없습니다.
참고문헌 (29)
참고문헌 신청
Q. Wang / 2011 / A robust and efficient algorithm for distributed compressed sensing / Computers &Electrical Engineering 37(6):916~926
H. Palangi / 2017 / Convolutional deep stacking networks for distributed compressive sensing / Signal Processing 131:181~189
Y. Oktar / 2018 / A review of sparsity-based clustering methods / Signal Processing 148:20~30
L. Vidya / 2015 / RBF network based sparse signal recovery algorithm for compressed sensing reconstruction / Neural Networks 63:66~78
X. Li / 2017 / A gradient-based approach to optimization of compressed sensing systems / Signal Processing 139:49~61
H. Palangi / 2017 / Convolutional deep stacking networks for distributed compressive sensing / Signal Processing 131:181~189
Y. Oktar / 2018 / A review of sparsity-based clustering methods / Signal Processing 148:20~30
L. Vidya / 2015 / RBF network based sparse signal recovery algorithm for compressed sensing reconstruction / Neural Networks 63:66~78
X. Li / 2017 / A gradient-based approach to optimization of compressed sensing systems / Signal Processing 139:49~61
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