One of the ways to estimate aerodynamic coefficients and derivatives is flight testing and using real flight data, which in this method distinguish real data from noise-tainted data, which has a great effect on the estimation accuracy. In this paper, a new method using time-frequency analysis of wavelet transform data is proposed to reduce noise from flight data. Robustness against flight conditions and no need for system modeling are advantages of this method compared to existing methods. The performance of the proposed method is verified by applying it to two types of data: six degrees of freedom simulation data and flight test data of an unmanned bird. The results show that the proposed method has a high capability in extracting real data against noise and external factors or non-linear behavior of the bird in turbulent weather conditions, which can be used to estimate the parameters of the bird with high accuracy. In this research, first, the algorithm was applied on the simulation data of six degrees of freedom, which were affected by noise, and it was somehow validated by comparing the original data with the cleaned data of the algorithm. Next, it was applied on the flight data of the unmanned bird and from The cleaned data have been used to extract the longitudinal and transverse transformation functions of the bird and analyze the stability and extract the frequency of its different modes.