Abstract: The modeling of flight path fitting is always one of the key problems in the research of combat agent trai-ning.Aiming at the low precision of track fitting in current combat multi-agent simulation training,a training strategy based on improved enhanced cyclic neural network and cubic spline interpolation was proposed.Taking the pitch angle,rolling angle and yaw angle of the aircraft as the reference objects,the track in the training process is fitted based on cubic spline interpolation algorithm,the error is reduced by cyclic neural network training,and the track is fitted.A large number of simulation experiments and the final engineering practice show that the method has higher accuracy and rationality than the existing track simulation algorithms.Under the same background,the track length decreases by nearly 10 percentage points,and the accuracy is more than 5 percentage points higher than the algorithm in the same field.The proposed algorithm can effectively solve the problem of combat agent in the same background.In simulation training,the track and actual operational error are reduced.

Key words: Combat agent, Cubic spline interpolation, Improved loop network, Track fitting