  In the field of Structural Dynamics, modal analysis is the foundation of
System Identification and vibration-based inspection. However, despite their
widespread use, current state-of-the-art methods for extracting modal
parameters from multi-input multi-output (MIMO) frequency domain data are still
affected by many technical limitations. Mainly, they can be computationally
cumbersome and/or negatively affected by close-in-frequency modes. The Loewner
Framework (LF) was recently proposed to alleviate these problems with the
limitation of working with single-input data only. This work proposes a
computationally improved version of the Lowner Framework, or iLF, to extract
modal parameters more efficiently. Also, the proposed implementation is
extended in order to handle multi-input multi-output data in the frequency
domain. This new implementation is compared to state-of-the-art methods such as
the frequency domain implementations of the Least Square Complex Exponential
method and the Numerical Algorithm for Subspace State Space System
Identification on numerical and experimental datasets. More specifically, a
finite element model of a 3D Euler-Bernoulli beam is used for the baseline
comparison and the noise robustness verification of the proposed MIMO iLF
algorithm. Then, an experimental dataset from MIMO ground vibration tests of a
trainer jet aircraft with over 91 accelerometer channels is chosen for the
algorithm validation on a real-life application. Excellent results are achieved
in terms of accuracy, robustness to noise, and computational performance by the
proposed improved MIMO method, both on the numerical and the experimental
datasets. The MIMO iLF MATLAB implementation is shared in the work
supplementary material.
