Title :
Compressed Sensing Performance of Random Bernoulli Matrices with High Compression Ratio
Author :
Weizhi Lu ; Weiyu Li ; Kpalma, Kidiyo ; Ronsin, Joseph
Author_Institution :
IETR, INSA de Rennes, Rennes, France
Abstract :
This letter studies the sensing performance of random Bernoulli matrices with column size n much larger than row size m. It is observed that as the compression ratio n/m increases, this kind of matrices tends to present a performance floor regarding the guaranteed signal sparsity. The performance floor is effectively estimated with the formula 1/2(√{πm/2} + 1). To the best of our knowledge, it is the first time in compressed sensing, a theoretical estimation is successfully proposed to reflect the real performance.
Keywords :
compressed sensing; matrix algebra; column size; compressed sensing performance; compression ratio; guaranteed signal sparsity; performance floor; random Bernoulli matrices; row size; theoretical estimation; Compressed sensing; Correlation; Electronic mail; Estimation; Materials; Sensors; Sparse matrices; Bernoulli distribution; compressed sensing; compression ratio; high dimension; random matrix;
Journal_Title :
Signal Processing Letters, IEEE
DOI :
10.1109/LSP.2014.2385813
