空间目标探测多传感器协同规划
2024,46(4):37-44
龙洗
国防科技大学 空天科学学院, 湖南 长沙 410073，longxi_1999@163.com
蔡伟伟
国防科技大学 空天科学学院, 湖南 长沙 410073
杨乐平
国防科技大学 空天科学学院, 湖南 长沙 410073
国防科技大学 空天科学学院, 湖南 长沙 410073，longxi_1999@163.com
蔡伟伟
国防科技大学 空天科学学院, 湖南 长沙 410073
杨乐平
国防科技大学 空天科学学院, 湖南 长沙 410073
摘要：
以多个地基雷达协同探测空间目标为背景,针对传统以整个可探测弧段为决策变量的协同规划方法存在的探测效率不高问题,建立了多传感器协同探测调度模型,提出了一种能够同时确定探测弧段和探测起始时间的自适应免疫遗传算法。考虑空间目标属性、类型、发射时间、雷达散射面积等级、用途等多种因素,构建了多层次模糊综合评价模型,并运用1-9标度法得到空间目标的优先级。以优先级最大化为目标,考虑探测时长、传感器容量等约束条件,采用自适应免疫遗传算法求解,并从探测资源消耗率和任务完成率两方面对规划方法性能进行评价。与改进的启发式算法以及传统进化算法的对比仿真表明,所提算法能够在提高任务完成率的同时降低资源消耗率。
以多个地基雷达协同探测空间目标为背景,针对传统以整个可探测弧段为决策变量的协同规划方法存在的探测效率不高问题,建立了多传感器协同探测调度模型,提出了一种能够同时确定探测弧段和探测起始时间的自适应免疫遗传算法。考虑空间目标属性、类型、发射时间、雷达散射面积等级、用途等多种因素,构建了多层次模糊综合评价模型,并运用1-9标度法得到空间目标的优先级。以优先级最大化为目标,考虑探测时长、传感器容量等约束条件,采用自适应免疫遗传算法求解,并从探测资源消耗率和任务完成率两方面对规划方法性能进行评价。与改进的启发式算法以及传统进化算法的对比仿真表明,所提算法能够在提高任务完成率的同时降低资源消耗率。
基金项目：
国防科技大学自主创新科学基金资助项目(22-ZZCX-083)
国防科技大学自主创新科学基金资助项目(22-ZZCX-083)
Multi-sensor cooperative planning of space objects detection
LONG Xi
College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China,longxi_1999@163.com
CAI Weiwei
College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China
YANG Leping
College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China
College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China,longxi_1999@163.com
CAI Weiwei
College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China
YANG Leping
College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China
Abstract：
Background of collaborative detection of space targets by multiple ground-based radars, to solve the issue of low detection efficiency in traditional collaborative planning methods that use the entire detectable arc segment as the decision variable, a multi-sensor collaborative detection scheduling model was established, and an adaptive immune genetic algorithm that could simultaneously determine the detection arc segment and detection start time was proposed. Considering various factors such as the space objects attribute, type, launch time, radar cross-section grade, and purpose, a multi-level fuzzy comprehensive evaluation model was constructed, and the 1-9 scale method was adopted to obtain the priority of the spatial target. In order to maximize the priority, consideringvarious constraints such as detection time, sensor capacity, and so on,an adaptive immune genetic algorithm was used to solve the problem.The performance of the planning method was evaluated from two aspects of detection resource consumption rate and task completion rate. By comparative analysis with the improved heuristic algorithm and traditional evolution algorithm, this algorithm improves the task completion rate while also reducing resource consumption rate.
Background of collaborative detection of space targets by multiple ground-based radars, to solve the issue of low detection efficiency in traditional collaborative planning methods that use the entire detectable arc segment as the decision variable, a multi-sensor collaborative detection scheduling model was established, and an adaptive immune genetic algorithm that could simultaneously determine the detection arc segment and detection start time was proposed. Considering various factors such as the space objects attribute, type, launch time, radar cross-section grade, and purpose, a multi-level fuzzy comprehensive evaluation model was constructed, and the 1-9 scale method was adopted to obtain the priority of the spatial target. In order to maximize the priority, consideringvarious constraints such as detection time, sensor capacity, and so on,an adaptive immune genetic algorithm was used to solve the problem.The performance of the planning method was evaluated from two aspects of detection resource consumption rate and task completion rate. By comparative analysis with the improved heuristic algorithm and traditional evolution algorithm, this algorithm improves the task completion rate while also reducing resource consumption rate.
收稿日期：
2022-03-02
2022-03-02
