Autonomous Maneuver Decision for Unmanned Aerial Vehicle via Improved Pigeon-Inspired Optimization

Abstract

As a crucial technology of air-to-air confrontation, autonomous maneuver decision has attracted wide attention in recent years. This paper proposes an improved pigeon-inspired optimization method to realize autonomous maneuver decision for unmanned aerial vehicles (UAVs) rapidly and accurately in an aerial combat engagement. The maneuver library is designed, including some advanced offensive and defensive maneuvers. A dependent set of trial maneuvers is generated to help UAVs make decisions in any tactical situation, and a future engagement state of the opponent UAV is predicted for each trial maneuver. The core of the decision-making process is that the objective function to be optimized is designed using the game mixed strategy, and the optimal mixed strategy is obtained by the improved pigeon-inspired optimization. A comparative analysis with other classical optimization algorithms highlights the advantage of the proposed algorithm. The simulation tests are conducted under four different initial conditions, namely, neutral, offensive, opposite, and defensive conditions. The simulation results verify the effectiveness of the proposed autonomous maneuver decision method.