This article extends the concept of compressed sensing to signals that are not sparse in an orthonormal basis but rather in a redundant dictionary. It is shown that a matrix, whic...
Holger Rauhut, Karin Schnass, Pierre Vandergheynst
Due to multipath delay spread and relatively high sampling rate in OFDM systems, the channel estimation is formulated as a sparse recovery problem, where a hybrid compressed sensi...
For compressive sensing, we endeavor to improve the recovery performance of the existing orthogonal matching pursuit (OMP) algorithm. To achieve a better estimate of the underlyin...
Saikat Chatterjee, Dennis Sundman, Mikael Skoglund
We investigate the problem of reconstructing sparse multivariate trigonometric polynomials from few randomly taken samples by Basis Pursuit and greedy algorithms such as Orthogona...