System identification is generally the art of mathematical modeling, given input - output measurements from a dynamical system. The problem is of interest in a variety of applications, ranging from chemical process simulation and control to identification of vibrational modes in flexible structures. Some identification methods in the area of flight dynamics have been considered and verified, such as the methods of the error equation [
] and the error equation in the frequency domain [
] and other methods. Systematic modal identification methods such as the Eingensystem Realization Algorithm (ERA) have advanced the complexity of structures that can be modeled by experimental measurements. The extended Eigensystem Realization Algorithm (EERA) method is a modified form of an ERA and calculates the modal parameters by manipulating both input and output time histories. The block Hankel matrices are built directly from the system input and output time history database. The development of these subspace identification methods is motivated by difficulties in estimating modal parameters for multiple-input multiple-output vibratory systems. To verify the performance of the EERA algorithm were realized an assessment for the identification of modal parameters of a Simulated Aircraft. The A4-D aircraft has been simulated using Simulink of Matlabfor some flight conditions such as varied altitude and Mach number. Amongst some models that describe the flight dynamics of an aircraft, the linear and non-linear mathematical model presented by [
] was adopted. Using the EERA method, the natural frequencies and damping factors of the implemented linear model were calculated from the simulation responses and were compared with the modal parameters obtained analytically. Then, using the simulation responses of the non linear model, the A4-D aircraft modal parameters were identified and compared with those of the linear model. The results obtained in the identification of the modal parameters of the non linear model did not present significant differences from the linear ones. This has shown that the EERA method is efficient enough in modal parameters estimation.