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This paper presents an approach for generating linear time invariant state-space models of a small Unmanned Air System. An instrumentation system using the robot operating system with commercial-off-the-shelf components is implemented to record flight data and inject auto- mated excitation signals. Offline system identification is conducted using the Observer/Kalman Identification algorithm to produce a discrete-time linear time invariant state-space model, which is then converted to a continuous time-model for analysis. Challenges concerning data collection and inverted V-Tail modelling are discussed, and solutions are presented. Longitudiunal, lateral/directional and combined longitudinal lateral/directional models of the test vehicle are generated using both manual and automated excitations, and are presented and compared. The generated longitudinal and lateral/directional results are compared to results for a small Unmanned Air System with a standard empennage. Flight test results presented in the paper show decent matching between the decoupled longitudinal and lateral/directional model and the combined longitudinal/lateral directional model.