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In this study, we consider real observation scenarios and propose an efficient method to accurately distinguish drones from birds using features obtained from their micro-Doppler (MD) signatures. In the simulations conducted using a rotating-blade model and a flapping-wing model, the classification result degraded significantly due to the diversity of both drones and birds, but a combination of features obtained for longer observation times significantly improved the accuracy. MD bandwidth was found to be the most efficient feature, but sufficient observation time was required to exploit the period of time-varying MD as a useful feature.