engleski

Spline-Like Chebyshev Polynomial Representation for Compressed Sensing

Compressive sensing is a technique for signal sampling below the Nyquist rate based on the assumption that the signal is sparse in some transform domain. The acquired signal is already in a compressed form and is appropriate for storage, transmission and processing. In this extended abstract, use of Chebyshev polynomials on intervals for efficient representation of one-dimensional continuous signals is proposed. The obtained parametric model fits into the compressive sensing paradigm and suits the need of the efficient processing of analog data on a digital computer.

rate of innovation, approximation theory, polynomial representation, optimization problem

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