Abstract
The occupancy guidance of Unmanned Aerial Vehicle (UAV) is one of the inevitable stages in the future air combat. Considering both constraints of missile launch condition and UAV flight performance, this paper established a multi-objective optimization model of occupancy guidance for UAV autonomous pursuit of enemy aircraft. The distance, angle and speed of occupancy guidance state are used as optimization variables to construct the advantage evaluation function. Based on the traditional heuristic algorithm, the Gradient Descent-Truncated Symbiotic Organizations Search (GDT-SOS) is designed to achieve rapid convergence of fitness values. By both numerical simulation and field test, the experiments demonstrated that UAV can autonomously achieve continuous, rapid and advantageous occupancy guidance under different dynamic initial conditions, which verifies that GDT-SOS has more effective than the comparison algorithm.
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