Near real-time characterization of unknown missiles in flight using computational intelligence

This paper focuses on the rapid characterization and identification of missiles of both known and unknown types early in their trajectories. Many physical relationships in the field of aerospace and aeronautics can be used to create computational models to calculate characteristics of an aerodynamic system. In such cases, the models created are generally more computationally intensive for problems of higher complexity. For example, computational fluid dynamics (CFD) yields high-fidelity solutions at the expense of long computation times. In a missile defense scenario where seconds are critical, computations must be performed much more rapidly than CFD while maintaining a high level of accuracy. In the event that an adversarial missile system of an unknown type is launched, there is an even greater need for rapid characterization. Computational intelligence methods provide a means to determine the underlying relationships between sets of data that may not be obvious to a human observer, such as between missile kinematic data and missile geometric data. Once the computational cost of training the algorithms has been invested, the calculation time per new solution is reduced to the order of milliseconds, enabling near real-time applications. In this study, we adapted a mature artificial neural network ensemble (ANNE) methodology originally developed for pharmaceutical research to accurately predict the diameter of missiles based on critical points of their ballistic trajectories, such as the state of the missile at motor burnout. The results indicate that it is feasible to determine geometric parameters, such as missile diameter, based on sparse telemetry data while a missile is still in flight. We expect that additional independent models can be trained for fineness ratio and other geometric measures. We also expect that the difficulties that early boost termination and other forms of “sandbagging” present for diameter prediction can be overcome. With further enhancement, this methodology could serve as an additional input to current missile defense algorithms, and is particularly well-suited as a supplementary process for characterizing previously unknown missiles.