基于MTPM和DPM的多无人机协同广域目标搜索滚动时域决策

传统的协同搜索决策方法在目标引导和机间协同方面存在不足. 研究建立了基于分布概率预测的目标概率图（Target probability map，TPM）初始化方法和基于贝叶斯准则的目标概率图动态更新方法，形成了修正目标概率图（Modified TPM，MTPM）及其运算机理.考虑对任务子区域进行可控回访，定义了数字信息素图（Digital pheromone map，DPM），建立了数字信息素图使用方法及更新机理.设计了基于MTPM和DPM的寻优指标，建立了基于滚动时域控制的协同搜索决策方法（MTPM-DPM-RHC method，MDR）.仿真表明: 1） MTPM能有效降低对目标的虚警率和漏检率；2） DPM能有效实现对任务区域可控回访；3） MDR方法的遍历能力、重访能力和目标搜索效率均优于已有方法.

Receding Horizon Decision Method Based on MTPM and DPM for Multi-UAVs Cooperative Large Area Target Search

There exist some shortcomings in both target directing mechanism and UAV cooperating mechanism in the traditional search decision making methods. A distribution probability prediction based initializing method for target probability map (TPM) is established, and a TPM refreshing method based on Bayesian rules is established, so that a modified TPM (MTPM) and its calculating mechanism are constituded. Then, in account of controllable revisit to sub mission areas, the notion of digital pheromone map (DPM) is established, and the usage strategy and calculation mechanism for DPM are designed. After that, the optimizing indexes based on MTPM and DPM are designed, and a new MTPM-DPM-RHC based decision making method (MDR) for UAV cooperative search is established. Simulation results prove that: 1) MTPM is able to decrease the target false alarm rate and missing rate effectively; 2) DPM is able to realize controllable revisit to the sub areas effectively; 3) As for the MDR method, the traversing ability, the revisiting ability and the target search efficiency are superior to the existing methods.