In this study, in-flight modal identification analyses are made based on vibration data collected during a flight test of an aircraft, by using two different output-only identification techniques: frequency domain decomposition and data-driven stochastic subspace identification. The purpose of this study was to evaluate and compare the efficacy of the two methods in modal parameter estimation and to validate their capability in dealing with some challenging tasks such as time tracking of modal parameters and estimating modal damping ratios. In addition, the effects of different environmental conditions and maneuvers are investigated by separating the flight-test data, such as static engine start, taxi, takeoff, cruise, roll, climb, descend, and yaw maneuvers. It is demonstrated that the selection of operational conditions and maneuvers plays a crucial role in identifying the modal parameters of the aircraft.

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