Iterative learning identification of aerodynamic drag curve from tracking radar measurements

The aerodynamic drag coefficient curve of spin-stabilized projectiles is very important to the fast generation of accurate firing tables. To identify it from Doppler tracking radar measured velocity data in flight tests, an iterative learning concept (ILC) is applied. High-order ILC algorithms are proposed. Convergence conditions are given in a general problem setting. A 3-DOF point mass trajectory prediction model is proposed. The learning gains, which vary with respect to both time and iteration number, have been used for a faster convergence compared to the constant learning parameter choices. Furthermore, in this paper, a bi-linear ILC scheme is proposed to produce even faster learning convergence. The flight testing data reduction results of an actual firing practice demonstrate that the iterative learning method is very effective in curve identification.