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In this work, we propose a novel missile guidance algorithm that combinesdeep learning based trajectory prediction with nonlinear model predictivecontrol. Although missile guidance and threat interception is a well-studiedproblem, existing algorithms' performance degrades significantly when thetarget is pulling high acceleration attack maneuvers while rapidly changing itsdirection. We argue that since most threats execute similar attack maneuvers,these nonlinear trajectory patterns can be processed with modern machinelearning methods to build high accuracy trajectory prediction algorithms. Wetrain a long short-term memory network (LSTM) based on a class of simulatedstructured agile attack patterns, then combine this predictor with quadraticprogramming based nonlinear model predictive control (NMPC). Our method, namednonlinear model based predictive control with target acceleration predictions(NMPC-TAP), significantly outperforms compared approaches in terms of missdistance, for the scenarios where the target/threat is executing agilemaneuvers.

Nonlinear Model Based Guidance with Deep Learning Based Target Trajectory Prediction Against Aerial Agile Attack Patterns