带有专家信度的无人机任务分配最小风险问题

minimum-risk problem of unmanned aerial vehicle task allocation with expert belief degree

战场环境中不确定因素的存在往往导致确定条件下获得的无人机任务分配方案不可行或者非最优,而传统期望值模型通常适应于长期规划,难以考虑不确定变量波动对某次决策的影响.针对目标价值不确定的无人机任务分配问题,首先,基于不确定理论建立以信度函数为目标的最小风险模型;然后,通过引入不确定向量的两种假设,将上述模型转化为带有分式目标函数的优化问题;最后,定义以比率为特征的辅助函数,并推导其单调性等性质,提出求解最小风险解的比率一维搜索直接算法.实验结果表明,与期望值模型相比,所提出的最小风险模型及其算法能规避不确定变量标准差较大的侦察目标点,并可通过调整预设收益获得多种不同信度水平的供选择方案.

Results of the UAV task allocation in the state of determinacy always become unfeasible or suboptimal when suffering from the uncertain factors in battlefield. The traditional expected value model is usually suitable for long-term planning, more difficult to consider the effect on certain decision making by the fluctuation of the uncertain variables. To deal with the uncertain programming in the UAV task allocation,the minimum-risk model is firstly established with a belief degree objective function based on uncertainty theory. Then, the model is transformed into an optimization problem with fractional objective by introducing two kinds of assumption. Finally, an auxiliary function is defined, and a ratio one-dimensional search exact algorithm is designed according to the monotonicity.The results of simulations show that the proposed method can keep the UAV away from the targets with larger standard deviation, and provide numerous alternative schemes with difficult belief degree levels by adjusting the target profit.