With the rise of technology, mobile unmanned target vehicles have become common in military training. These vehicles carry shooting targets, offering troops mobile targets for shooting practice and enhancing simulated training effectiveness. However, in complex terrains, the movement path of unmanned vehicles falls short of achieving desired results. Path planning for unmanned target vehicles has therefore gained importance. To optimize their movement and enhance military training, a robust adaptive positioning algorithm based on Global Positioning System/Bei Dou System (GPS/BDS) technology is proposed. This algorithm ensures precise positioning and trajectory prediction. Additionally, the Ant Colony Optimization (ACO) algorithm is improved by considering heuristic factors and defining restricted regions, optimizing the trajectory. Simulation experiments demonstrate high-precision navigation and positioning, reducing signal propagation time and improving smoothness and directionality. The path length approximates the optimal path with minimal error. Comparative experiments confirm the algorithm's accuracy and efficiency in planning paths with speed, fewer turns, and improved smoothness. This optimization helps unmanned target vehicles enhance their trajectories and improve military training effectiveness.

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