  Consider a spectrally sparse signal $\boldsymbol{x}$ that consists of $r$
complex sinusoids with or without damping. We study the robust recovery problem
for the spectrally sparse signal under the fully observed setting, which is
about recovering $\boldsymbol{x}$ and a sparse corruption vector
$\boldsymbol{s}$ from their sum $\boldsymbol{z}=\boldsymbol{x}+\boldsymbol{s}$.
In this paper, we exploit the low-rank property of the Hankel matrix formed by
$\boldsymbol{x}$, and formulate the problem as the robust recovery of a
corrupted low-rank Hankel matrix. We develop a highly efficient non-convex
algorithm, coined Accelerated Structured Alternating Projections (ASAP). The
high computational efficiency and low space complexity of ASAP are achieved by
fast computations involving structured matrices, and a subspace projection
method for accelerated low-rank approximation. Theoretical recovery guarantee
with a linear convergence rate has been established for ASAP, under some mild
assumptions on $\boldsymbol{x}$ and $\boldsymbol{s}$. Empirical performance
comparisons on both synthetic and real-world data confirm the advantages of
ASAP, in terms of computational efficiency and robustness aspects.
