Abstract: We propose a linear-filter-based adaptive tracking strategy to address parametric uncertainty in quadrotors. First, we decouple the dynamic equations of quadrotors into two independent subsystems of attitude and height. Then, we modify these subsystems into linear parameterization format, and design laws for control and parameter adjustment. We utilize Lyapunov-based theories to analyze the stability of the closed loop, which gurantees the asymptotic convergence of tracking errors and their first-order derivatives. In addition, we analyze the convergence of the parameter estimation from the system identification perspective. We then compare and verify the effective performance of our proposed control scheme with that of a feedback linearization controller based on the nominal model. Simulation results show that the estimated parameters can converge to their true values when the persistent excitation condition is satisfied.