A New Discrete-time Guidance Law Base on Trajectory Learning and Prediction

Abstract

A new data-driven predictive discrete-time guidance law is presented for an interceptor pursuing a target which can perform arbitrary maneuver. The designed guidance law is driven by observed data of certain steps, which record previous positions of the target and make it feasible to estimate the behavior of the target and hence design the guidance command at each step by solving an time-dependent optimization problem, and this feature distinguishes the proposed guidance law from those traditional guidance laws which are usually described by an ordinary differential equations and use only the measurement at current time instant. To verify the performance of the new guidance law proposed, extensive simulations were carried out to compare it with some typical existing guidance laws like pursuit guidance (PG), beamer rider (BR) guidance, constant bearing (CB) guidance and proportional navigation (PN) law. The simulation studies show that the new predictive guidance law (abbreviated as LP) can provide comparative performance in all the cases studied, and it can even outperform other guidance laws when the target performs random maneuver, which show that the proposed guidance scheme exhibits certain robustness and adaptation.