Semi-deterministic ternary matrix for compressed sensing

Author

Weizhi Lu ; Kpalma, Kidiyo ; Ronsin, Joseph

Author_Institution

IETR, Univ. Eur. de Bretagne (UEB), Rennes, France

fYear

2014

fDate

1-5 Sept. 2014

Firstpage

2230

Lastpage

2234

Abstract

For the random {0,±1} ternary matrix, it is interesting to determine the number of nonzero elements required for good compressed sensing performance. By seeking the best RIP, this paper proposes a semi-deterministic ternary matrix, which is of deterministic nonzero positions but random signs. In practice, it presents better performance than common random ternary matrices and Gaussian random matrices.

Keywords

compressed sensing; matrix algebra; compressed sensing; nonzero elements; semideterministic ternary matrix; Compressed sensing; Eigenvalues and eigenfunctions; Indexes; Sensors; Sparse matrices; Symmetric matrices; Vectors; RIP; compressed sensing; deterministic; random matrix; semi-deterministic; ternary matrix;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing Conference (EUSIPCO), 2014 Proceedings of the 22nd European

Conference_Location

Lisbon

Type

conf

Filename

6952806

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=149549