  Rank deficient Hankel matrices are at the core of several applications.
However, in practice, the coefficients of these matrices are noisy due to e.g.
measurements errors and computational errors, so generically the involved
matrices are full rank. This motivates the problem of Hankel structured
low-rank approximation. Structured low-rank approximation problems, in general,
do not have a global and efficient solution technique. In this paper we propose
a local optimization approach based on a two-levels iteration. Experimental
results show that the proposed algorithm usually achieves good accuracy and
shows a higher robustness with respect to the initial approximation, compared
to alternative approaches.
