基于DPSO-SA的低轨预警系统初始任务规划方法

为提高天基低轨预警系统在导弹跟踪任务中的效率,建立了天基低轨预警系统初始任务规划模型.该模型包含跟踪精度、任务完成率和资源松弛度等优化指标,考虑导弹跟踪中目标信息的不确定性,定义并构建了跟踪原子任务的不确定度和动态优先级.在此基础上,提出采用离散粒子群(DPSO,Discrete Particle Swarm Optimization)-模拟退火(SA,Simulated Annealing)混合优化算法求解初始任务规划模型,提高了算法收敛速度、精度以及全局搜索能力.仿真算例验证了模型的优点以及DPSO-SA混合优化算法的有效性.

Original task planning method of early warning system of LEO based on DPSO-SA

To improve the resource efficiency for missile tracking with early warning system of low-earth-orbit(LEO), the original task planning model of early warning system of LEO was set up. The model includes the optimizing indexes of tracking precision, task accomplishment and resource slack. The dynamic priority of meta tracking task was defined and designed in view of the information uncertainty of missile object in tracking. Based on the model, a mixed discrete particle swarm optimization (DPSO) and simulated annealing (SA) algorithm was proposed to solve the original task planning problem. The algorithm improves the seeking and solving ability for the global optimal result. Simulation results show the advantage of the model and the validity of DPSO-SA algorithm.