关键词: 反炮兵作战, 无人机集群, Markov状态模型, 搜索路径规划

Abstract:

Modern artillery has the characteristics of maneuver combat, quick-hitting and fast-retreating. It is difficult to hit it directly based on the position provided by the artillery reconnaissance radar. Therefore, a method for planning search paths for unmanned aerial vehicle (UAV) swarm to against artillery based on the prediction of the transfer path of artillery is proposed. The proposed method improves artillerg discover and strike effciency of UAV swarm by predicting the transfer path of artillery after shotting. The model of the battlefield environment and combat elements is established, and the concept of combat state and geomorphic environment suitability are proposed. The Markov state model of artillery combat is utilized to combine the combat state and geomorphic environment suitability for artillery transfer path prediction. An UAV swarm search path planning algorithm is constructed based on rolling time-domain optimization, and the search path optimization problem under sparse pheromone distribution is solved through the newly designed expected reward term in the objective function. The results of the simulation demonstrate that the proposed method is capable of effectively utilizing battlefield information to predict the possible transfer paths of blue artillery. In comparison to other methods for comparison, the proposed method demonstrates certain advantages in terms of effectiveness and stability in search and strike tasks, which provides a basis for subsequent practical applications.

Key words: anti-artillery combat, unmanned aerial vehicle (UAV) swarm, Markov state model, search path planning