Abstract: Optimal input design enhances aerodynamic parameter identification accuracy for aircraft closed-loop systems. This paper adressed the optimal criterion-based input design problem for standard aircraft aerodynamic parameter identification. The design of global optimization of Fisher information inverse matrix's metric can help obtain the optimal input signal. Simple analysis or experience in many research works choose the matrix's metric. We used an aerodynamic validation standard model aircraft (AVM) cruise flight scenario to analyze the correlation between typical optimal criteria and parameter identification accuracy. The results show that the inverse matrix's trace and condition number correlate relatively more strongly with identification accuracy than other metrics. Thus, we proposed an Ac-criterion and use the particle swarm global algorithm to find the optimal parameter of an input signal to minimize the Ac-criterion in a given parameter range. Three excitation signals, namely, "3211", dipole square wave, and multi-sine quadrature signal, were designed by the Ac-optimal method and the traditional frequency band analysis. The frequency band method uses the natural frequency of the aircraft directly to design the input signal's period and the empirical amplitude. Then, we utilized the designed signals to excite the AVM closed-loop's dynamic characteristics and generated flight simulation data to identify the aircraft's longitudinal aerodynamic stability and control derivatives. The Ac-optimal design method's effectiveness was evaluated by the estimated results' root-mean-square errors relative to the reference derivatives. The results show that the Ac-criterion-based optimization method is more efficient than the frequency band design method in finding the optimal signal to excite the closed-loop's dynamic characteristics. Compared with the band-designed results, the Ac-optimal-designed signals improve the total accuracy of the five longitudinal control derivatives by 27.6%, 91.54%, and 64.4%, respectively. This enhancement is mainly due to the Ac-optimal-designed signals significantly improves the identification accuracy of the lift control derivative. This paper verified that criterion correlation analysis can help improve the input signal's excitation effect on the dynamic systems, and the proposed Ac-criterion-based design method is effective in obtaining the optimal input excitation signals. This method has some application values for identifying aerodynamic parameters for aircraft closed-loop systems.