Generation of dual missile strategies using genetic algorithms

Abstract

The use of multiple missiles in order to improve the kill probability of a target is studied. The use of the same guidance law or strategy for two missiles fired from approximately the same position does not make the best use of the two to one numerical advantage during the engagement. The use of different guidance strategies is put forward as a method to improve the kill probability. The objective is to produce different intercept trajectories for the two missiles. In this study a medium to short range air-to-air engagement scenario using two active mono-pulse radar based homing missiles is considered. A genetic algorithm (GA) is used to generate two guidance laws which produce different trajectories for intercept and also improve the overall performance of the two missile system. The individual guidance laws produced by the GA are implemented using radial basis function neural networks (RBFN). The laws generate significantly different trajectories for the two missiles, producing a combination of side on and head on intercepts in some scenarios. Their performance and robustness is demonstrated and compared to two modern guidance laws by simulation. The dual RBFN laws are shown to outperform the two analytical laws and have a similar level of robustness.