多监测任务下空间碎片监测网调度优化方法

A Scheduling Optimization Method for Space Debris Monitoring Network in Multiple Monitoring Task Scenarios

空间碎片监测网采集的轨道测量数据是轨道编目的基础。面对巨量碎片和有限的监测站点,数据采集方法与快速的作业任务调度优化是充分发挥监测效能、提升编目能力和精度的关键技术。监测任务包括常规监测、重点目标监测和应急监测等。针对监测网多任务调度优化问题,以监测收益为目标函数,分别提出线性指派模型和考虑移动成本的非线性指派模型,并使用改进的LAPJV算法和改进的2-opt算法进行解算。开展了地基观测网络空间碎片监测任务优化仿真实验,线性模型和非线性模型处理200个测站、7170个碎片的4 h任务规划,改进的LAPJV算法和2-opt算法的解算时间分别为12.051 s和162.071 s,监测总收益分别为289399.07和285333.79,分别可监测2931和2918个碎片,占碎片总数的40%以上。结果表明,模型/算法兼顾解算速度和精度,具有近实时监测任务优化的能力,可以作为监测任务优化的有效解决方案。

Orbit measurement data collected by a space debris monitoring network is the basis of debris orbit cataloguing.With the vast number of debris objects but limited monitoring facilities,data collection strategy and task programming are the key technologies to bring into full play of monitoring facilities and improve cataloguing capability and accuracy.The monitoring tasks include routine monitoring,important object monitoring and monitoring of emergent events.For the multi-task scheduling optimization problem of a monitoring network,this paper proposes a linear assignment model and a nonlinear assignment model considering move cost,respectively,in which the monitoring benefit is defined as the objective function,and uses the improved LAPJV algorithm and improved 2-opt algorithm to solve them.The optimization experiments with ground-based simulated debris monitoring networks are carried out,in which the improved LAPJV algorithm and 2-opt algorithm have a solution time of 12.051 s and 162.071 s,respectively,and the total benefit is 289399.07 and 285333.79,respectively in term of the linear model and the nonlinear model with 4-hour task scheduling for 200 stations and 7170 debris.The final results show that both the algorithms are able to solve near real-timely the monitoring task programming,and can be used as an effective solution to the network task scheduling optimization.