Skip to Main Content
In this paper, current work on the Aerodynamic Coefficient Estimation (ACES) program for guided missiles is reviewed. A fundamental statistical approach to the problem is taken, and recent developments in the identification of model structure are used including: initial comparison of parametric model structures by subset regression using a leaps and bounds algorithm, refined comparison of different parametric model structures using the Akaike information criterion (AIC), and estimation of parameters within a model structure using batch and recursive maximum likelihood parameter estimation algorithms. Detailed numerical results of the parameter estimation procedures are presented using simulated measurement data. Batch maximum likelihood (BML) and recursive maximum likelihood (RML) parameter estimation algorithms are compared to the original ACES program using the Extended Kalman Filter (EKFP) algorithm with the unknown parameters augmenting the states. It is found that the BML is about 100 times more accurate than the EKF, and that the RML is almost as accurate as the BML with considerably less computation.