Due to advancing technology, systems in engineering are becoming increasingly complex
and interconnected. Despite the ubiquity of systems that can be modelled as interconnected …

In this paper, we revisit the problem of identifying multi-input/multi-output linear-time-
invariant discrete-time systems from measured power spectrum data on uniform grids of …

In this paper, a regularized nuclear norm minimization-based subspace identification
algorithm is proposed to estimate discrete-time power spectra of multi-input/multi-out …

Efficient and optimal prediction of frozen flow turbulence using the complete observation
history of the wavefront sensor is an important issue in adaptive optics for large ground …

Subspace-based methods have been effectively used to estimate multi-input/multi-output,
discrete-time, linear-time-invariant systems from noisy spectrum samples. In these methods …

The relationship between the regularization methods proposed in the literature to increase
the robustness of the filtered-X LMS (FXLMS) algorithm is discussed. It is shown that the …

In this paper, we propose a subspace algorithm for the identification of linear-time-invariant
discrete-time systems with more outputs than inputs from measured power spectrum data …

Subspace-based methods have been effectively used to estimate multi-input/multi-output,
discrete-time, linear-time invariant systems from uniformly spaced spectrum samples. A …

This article investigates the use of subspace methods for spectral factorization of non-
parametric frequency domain data. The techniques developed in this paper augment the …

In this paper, frequency-domain subspace-based algorithms are proposed to estimate
discrete-time cross-power spectral density (cross-PSD) and auto-power spectral density …