  High quality measurements are paramount to a successful application of
experimental techniques in structural dynamics. The presence of noise and
disturbances can significantly distort the information stored in the data and,
if not adequately treated, may result in erroneous findings and misleading
predictions. A common technique to filter out noise relies on decomposing the
dataset into singular components sorted by their degree of significance.
Discarding low-value contributions helps to clean the data and remove
spuriousness. This paper presents PRANK, a novel singular value-based
reconstruction approach for multiple response vibration datasets. PRANK
integrates the effect of Principal Response Functions and Hankel filtering
actions, resulting in an improved data reconstruction for both system poles and
zeros. The proposed formulation is tested on both analytical and numerical
examples, showcasing its robustness, efficiency and versatility. PRANK operates
with both time- and frequency-based data. Applied to noisy full-field camera
measurements, the filter delivered excellent performance, indicating its
potential for various identification tasks and applications in vibration
analysis.
