In this study, stability derivatives of a small unmanned aerial vehicle (UAV), which is fixed-wing aircraft whose wingspan and weight are approximately 1 m and 1 kg, respectively, are estimated accurately by a novel system identification method called wavelet filtered regression (WFR). Conventional system identification techniques such as filter error methods (FEM) have been used to obtain the stability derivatives of general aircraft; however, they cannot estimate those of the small UAV accurately, because small UAV flight is easily disturbed by process noise represented by wind gust. WFR has robustness against such disturbance by using time-frequency information provided by multi resolution analysis (MRA) of wavelet transform. The comparisons of WFR and FEM estimation results clearly show the advantages of WFR. The resemblance between WFR and subspace identification methods is also described.
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