`Approximate message passing' algorithms proved to be extremely effective in reconstructing sparse signals from a small number of incoherent linear measurements. Extensive num...
— We study the inversion of a random field from pointwise measurements collected by a sensor network. We assume that the field has a sparse representation in a known basis. To ...
In recent work, Kalman Filtered Compressed Sensing (KF-CS) was proposed to causally reconstruct time sequences of sparse signals, from a limited number of “incoherent” measure...
Compressive Sensing (CS) uses a relatively small number of non-traditional samples in the form of randomized projections to reconstruct sparse or compressible signals. The Hough t...
Ali Cafer Gurbuz, James H. McClellan, Justin K. Ro...
Compressive sensing (CS) is an emerging approach for acquisition of signals having a sparse or compressible representation in some basis. While CS literature has mostly focused on...