  In this work, we propose a novel missile guidance algorithm that combines
deep learning based trajectory prediction with nonlinear model predictive
control. Although missile guidance and threat interception is a well-studied
problem, existing algorithms' performance degrades significantly when the
target is pulling high acceleration attack maneuvers while rapidly changing its
direction. We argue that since most threats execute similar attack maneuvers,
these nonlinear trajectory patterns can be processed with modern machine
learning methods to build high accuracy trajectory prediction algorithms. We
train a long short-term memory network (LSTM) based on a class of simulated
structured agile attack patterns, then combine this predictor with quadratic
programming based nonlinear model predictive control (NMPC). Our method, named
nonlinear model based predictive control with target acceleration predictions
(NMPC-TAP), significantly outperforms compared approaches in terms of miss
distance, for the scenarios where the target/threat is executing agile
maneuvers.
