  We propose novel algorithms that enhance the performance of recovering
unknown continuous-valued frequencies from undersampled signals. Our iterative
reweighted frequency recovery algorithms employ the support knowledge gained
from earlier steps of our algorithms as block prior information to enhance
frequency recovery. Our methods improve the performance of the atomic norm
minimization which is a useful heuristic in recovering continuous-valued
frequency contents. Numerical results demonstrate that our block iterative
reweighted methods provide both better recovery performance and faster speed
than other known methods.
