  We present a general class of compressed sensing matrices which are then
demonstrated to have associated sublinear-time sparse approximation algorithms.
We then develop methods for constructing specialized matrices from this class
which are sparse when multiplied with a discrete Fourier transform matrix.
Ultimately, these considerations improve previous sampling requirements for
deterministic sparse Fourier transform methods.
