This paper addresses the issue of adaptive global optimization using Kriging metamodel known as EGO(Efficient Global Optimization). The algorithm adaptively chooses where to generate subsequent samples based on an explicit trade-off between reduction of global uncertainty and exploration of the region of the interest. A strategy that saves the computational cost by using expectations derived from probabilistic nature of approximate model is proposed. At every iteration, a candidate test point that seems to be feasible/inactive or has little possibility to improve for minimum is identified and excluded from updating approximate models. By doing that the computational cost is saved without loss of accuracy.

Estimation of a full set of aerodynamic coefficients for a flight vehicle is performed processing the actual flight test data. Extended Kalman filter algorithm is utilized. Dynamic plant modeling is set up including aerodynamic forces, thrust, and mass characteristics. The pre-processed test data are used as measurement inputs for the above filtering algorithm. Six component aerodynamic coefficient structure is modeled in a polynomial form. It contains a set of 30 independent coefficients which consist of static and dynamic derivatives in the longitudinal plane as well a s in the lateral-directional one. The derivatives are determined by the multidimensional algebraic polynomial approximation from the wind tunnel look-up tables. The effect of parameter identification has been proved to be useful by comparing some state variable trajectoties one another which are constructed from the identified parameters, the original wind tunnel data, and the telemetered flight test data.