Abstract: Traditional three-line attack area of medium-range air-to-air missile can’t provide effective tactical decision information for the pilot, and it has poor time effectiveness and low precision in calculation using fire control system. To address these problems, a new type of missile launch envelope, under the consideration of the target escape maneuver, is proposed with the attack aircraft as the center. In this paper, common and effective tactical maneuvers, which are used to escape from missiles successfully, are considered and analyzed through many classic examples of battles. The attack area is abstracted into seven killing envelops. the calculated methods are given and the offline simulations are carried out. The sample database is established by identifying eight motion parameters and envelop-values. By introducing deep learning methods, the Auto-Encoder (AE) network model is constructed. By adopting unsupervised learning, the primary features of the sample are extracted, and the high dimensional vectors describing nonlinearity in the sample database are obtained. The deep network model is established with high dimensional vectors to extract advanced features for fitting. Experimental results show that the loss of the deep network can be controlled within 15 m compared with the six-degree-of-freedom simulation results and the actual missile data. The online calculation time is only 0.04 s, which can meet the real-time requirements. The new missile envelope can provide timely and effective information for pilot to understand enemy situation and support maneuvering decision-making.

Key words: air to air missile, attack area, killing envelop, deep learning, auto-encoder network, real time