Adaptive fuze-warhead coordination method based on BP artificial neural network

Abstract

The appropriate fuze-warhead coordination method is important to improve the damage efficiency of air defense missiles against aircraft targets. In this paper, an adaptive fuze-warhead coordination method based on the Back Propagation Artificial Neural Network (BP-ANN) is proposed, which uses the parameters of missile-target intersection to adaptively calculate the initiation delay. The damage probabilities at different radial locations along the same shot line of a given intersection situation are calculated, so as to determine the optimal detonation position. On this basis, the BP-ANN model is used to describe the complex and highly nonlinear relationship between different intersection parameters and the corresponding optimal detonating point position. In the actual terminal engagement process, the fuze initiation delay is quickly determined by the constructed BP-ANN model combined with the missile-target intersection parameters. The method is validated in the case of the single-shot damage probability evaluation. Comparing with other fuze-warhead coordination methods, the proposed method can produce higher single-shot damage probability under various intersection conditions, while the fuze-warhead coordination effect is less influenced by the location of the aim point.

Keywords

• Aircraft vulnerability
• Fuze-warhead coordination
• BP artificial neural network
• Damage probability
• Initiation delay