Quick intention identification of an enemy aerial target through information classification processing

Rapidly and accurately identifying the tactical intention of an enemy aerial target is an important issue for combat decision making. To this end, a quick intention identification model based on hybrid neural network is established in this paper. With available feature sequential measurements of the enemy target as inputs of the model, possibilities of different intentions are calculated timely. To increase the training efficiency and accuracy of recognition, the measurement information is processed using different neural network. Maneuvering data with large variations over time are processed using gated recurrent unit (GRU), while other data are processed using back propagation (BP) neural network. Besides, the fitting cubic sample interpolation is adopted to deal with incomplete information. Monte Carlo simulations demonstrate the robustness and accuracy of the established model, and training comparison with conventional models shows that the proposed method has higher training efficiency and better identification performance.