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作为一种复杂多传感器跟踪任务,天基预警过程可视作一种多维离散时间序列监控与预测问题.预警任务具有高实时性、动态性、高低轨配合、多星协作等特点,因而调度模型需能够优化利用预警资源完成有效预警.本文从实际问题出发,主要阐述两项工作:第一,提出基于信息增益的多目标优化预警调度模型,第二,阐述免疫克隆选择算法,并给出一种分布式并行调度求解方法,以改善调度算法的收敛速度和鲁棒性,解决实际需要.最后,通过基于HLA的仿真系统,以美国SBIRS为背景,验证了本文调度模型和算法的有效性.
As a kind of complex multisensor tracking task, the space-based early-warning process can be regarded as a multi-dimensional discrete time series monitoring and forecasting problem.The early warning task has the characteristics of high real-time, dynamic, high and low rail cooperation, multi-star collaboration, It is necessary to optimize the use of early warning resources to complete the effective early warning.This article sets out from the practical problems, mainly elaborates two tasks: first, proposes a multi-objective optimization and warning scheduling model based on information gain; second, expounds immune clonal selection algorithm, In order to improve the convergence speed and robustness of the scheduling algorithm, the solution to the actual needs is proposed.Finally, the effectiveness of the proposed scheduling model and algorithm is verified by the HLA-based simulation system and the U.S. SBIRS.