Optimising the unpredictability of a ballistic missile

At MODSIM2023, the 25th International Congress on Modelling and Simulation, a Shoal team presented a paper called ‘Optimising the unpredictability of a ballistic missile’. The research was conducted by Emma Comino,  John Ogilvie, Matthew King, Henry Lourey and John Wharington. The research team constructed a physics-based model of a ballistic missile. The aerodynamic model was then fed into an optimisation scheme, which facilitated a quick quantitative comparison between the time it takes the missile to reach its target and the sacrifice it may make to obscure its true target. The model hence provides a proof of concept framework for exploring trade-offs between missile performance and missile evasion behaviours.

Read their paper: 2023 MODSIM – Abstract – Optimising the unpredictability of a ballistic missile.

View their presentation: 2023 MODSIM – Presentation – Optimising the unpredictability of a ballistic missile.

At the same conference, Shoal also presented a paper on ‘Machine learning–enabled missile system characterisation’.

Shoal has been supporting clients in Defence for more than two decades. For further insight into how Shoal uses Machine Learning, please contact us for a confidential discussion.