  A sparsity-driven algorithm of inverse synthetic aperture radar (ISAR)
imaging is proposed. Based on the parametric sparse representation of the
received ISAR signal, the problem of ISAR image formation is converted into the
joint estimation of the target rotation rate and the sparse power distribution
in the spatial domain. This goal is achieved by sequential minimization of L0
and L2 norms, which ensure the sparsest ISAR image and the minimum recovery
error, respectively.
