航空集群协同搜索马尔可夫运动目标方法

针对航空集群执行未知区域的协同搜索任务,提出一种分布式模型预测控制(distributed model predictive control,DMPC)下的贪婪迭代决策方法。该方法首先建立航空集群飞行器的运动模型,对其运动特性进行分析,而后采用搜索信息图模型,描述未知环境下动态目标随搜索过程变化的变化趋势;再用马尔可夫链表征目标隐潜运动,对其进行预测;最后在DMPC的基础上,采用随机决策序列下的贪婪迭代算法进行问题求解。并对所提方法的稳定性和收敛性进行分析。同时通过设计仿真实验,验证了该方法的可行性和优越性。

Collaborative aeronautic swarm search of Markov moving targets

To realize coordinated searching tasks of aeronautic swarms in unknown areas, a greedy iterative decision-making method based on distributed model predictive control (DMPC) is proposed. First, a swarm-aerial-vehicle movement model is built to analyze their moving characteristics, and the search information graph model is used to describe the changes of dynamic targets in unknown environment during the searching task. Then, the Markov chain is used to characterize and forecast the target's potential movements. Finally, on the basis of DMPC, a greedy iterative algorithm of the random decision-making sequence is adopted to find solutions. The stability and convergence of the proposed method are also analyzed. Simulation experiments prove the feasibility and superiority of the proposed method.