  The low-complexity assumption in linear systems can often be expressed as
rank deficiency in data matrices with generalized Hankel structure. This makes
it possible to denoise the data by estimating the underlying structured
low-rank matrix. However, standard low-rank approximation approaches are not
guaranteed to perform well in estimating the noise-free matrix. In this paper,
recent results in matrix denoising by singular value shrinkage are reviewed. A
novel approach is proposed to solve the low-rank Hankel matrix denoising
problem by using an iterative algorithm in structured low-rank approximation
modified with data-driven singular value shrinkage. It is shown numerically in
both the input-output trajectory denoising and the impulse response denoising
problems, that the proposed method performs the best in terms of estimating the
noise-free matrix among existing algorithms of low-rank matrix approximation
and denoising.
