Introduction - If you have any usage issues, please Google them yourself
Compressive sensing (CS) is a new approach to simultaneous sensing and compression of sparse
and compressible signals based on randomized dimensionality reduction. To recover a signal from its
compressive measurements, standard CS algorithms seek the sparsest signal in some discrete basis or
frame that agrees with the measurements. A great many applications feature smooth or modulated signals
that are frequency sparse and can be modeled as a superposition of a small number of sinusoids.
Unfortunately, such signals are only sparse in the discrete Fourier transform (DFT) domain when the
sinusoid frequencies live precisely at the center of the DFT bins. When this is not the case, CS recovery
performance degrades significantly. In this paper, we introduce a suite of spectral CS (SCS) recovery
algorithms for arbitrary frequency sparse signals. The key ingredients are an over-sampled DFT frame, a
signal model that inhibits closely spaced sinusoids, and classical sinusoid parameter e