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This paper develops a general framework for robust and efficient recovery of nonlinear but structured signal models, in which x is assumed to lie in a union of subspaces and the samples are modelled as inner products with an arbitrary set of sampling functions. 82 PDF View 1 excerpt Compressed Sensing of Analog Signals in Shift-Invariant Spaces The present study focuses on investigating the nonideal sampling in shift-invariant spaces associated with the quadratic-phase Fourier transforms. The primary aim is to formulate novel convolution
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Unfortunately, in practice, ideal sampling is impossible to implement. A more accurate model considers general-ized sampling [9]-[12]. Instead of point-wise evaluations of the continuous-time function, the samples are modelled by a set of inner-products between the continuous-time signal and the sampling functions. These sampling functions
sampling – creating a discrete signal from a continuous process. downsampling (decimation) – subsampling a discrete signal upsampling – introducing zeros between samples to create a longer signal aliasing – when sampling or downsampling, two signals have same sampled representation but differ between sample locations.