  Flutter flight test involves the evaluation of the airframes aeroelastic
stability by applying artificial excitation on the aircraft lifting surfaces.
The subsequent responses are captured and analyzed to extract the frequencies
and damping characteristics of the system. However, noise contamination,
turbulence, non-optimal excitation of modes, and sensor malfunction in one or
more sensors make it time-consuming and corrupt the extraction process. In
order to expedite the process of identifying and analyzing aeroelastic modes,
this study implements a time-delay embedded Dynamic Mode Decomposition
technique. This approach is complemented by Robust Principal Component Analysis
methodology, and a sparsity promoting criterion which enables the automatic and
optimal selection of sparse modes. The anonymized flutter flight test data,
provided by the fifth author of this research paper, is utilized in this
implementation. The methodology assumes no knowledge of the input excitation,
only deals with the responses captured by accelerometer channels, and rapidly
identifies the aeroelastic modes. By incorporating a compressed sensing
algorithm, the methodology gains the ability to identify aeroelastic modes,
even when the number of available sensors is limited. This augmentation greatly
enhances the methodology's robustness and effectiveness, making it an excellent
choice for real-time implementation during flutter test campaigns.
