The performance of compressed sensing (CS) algorithms is dependent on the sparsity level of the underlying signal, the type of sampling pattern used and the reconstruction method applied. The higher the incoherence of the sampling pattern used for undersampling, less aliasing will be noticeable in the aliased signal space, resulting in better CS reconstruction. In this work, based on point spre...

in this paper, a novel time-varying channel estimation approach based on Kalman filter compressive sensing is proposed for the high sampling problem of ultra wideband (UWB) system considering the sparse of the channel impulse response. The direct sequence UWB signal is formulated to the mathematical model of compressed sensing after down sampling. The receiver recovery the channel impulse respo...

Compressive sampling (CS), also called Compressed Sensing, has generated a tremendous amount of excitement in the image processing community. It provides an alternative to Shannon/Nyquist sampling when the signal under acquisition is known to be sparse or compressible. In this paper, we propose a new efficient image fusion method for compressed sensing imaging. In this method, we calculate the ...

The emergence of compressed sensing (CS) theory provides potential hardware architecture to sub-Nyquist sample the wideband signals. However, applying this discrete CS model to continuous analogue signals is not an easy task. The modulated wideband converter (MWC) is an efficient wideband compressed sampling architecture for the sparse multiband signals. In this paper, a soft-calibration system...