Two-Stage Rapidly-Exploring Maneuvering Target Pursuit Strategy for Radar-Enabled UAV

This paper explores the maneuvering target pursuit problem of the radar-enabled unmanned aerial vehicle (RUAV). The target pursuit problem is modeled as an autonomous sensing and decision-making process of the RUAV, in which the state of the target is estimated by the radar and is utilized as the environmental information to plan the flight trajectory of the RUAV. The cumulative distance between the RUAV and the target plus the length of the trajectory in multiple predictive steps is used as the utility function to characterize the expected revenue of the RUAV for the target pursuit task, and a dynamic optimization model with practical constraints is formulated on the basis of the model predictive control framework. Thetwo-stage rapidly-exploring maneuvering parget pursuit(TRMTP) algorithm consisting of multi-step target state prediction, global path generation, local trajectory optimization, and target state estimation is proposed, which integrates the interacting multiple model filter, optimal rapidly-exploring random tree, and receding-horizon particle swarm optimization to obtain the real-time path planning strategy while avoiding the forbidden region. Numerical results are provided to evaluate the effectiveness and superiority of the TRMTP algorithm over some typical counterparts in terms of optimality and completion time for the target pursuit mission.