系统工程与电子技术 ›› 2024, Vol. 46 ›› Issue (10): 3407-3415.doi: 10.12305/j.issn.1001-506X.2024.10.18

• 系统工程 • 上一篇    

天基信息支援体系建模与效能评估方法

陈宇1, 师鹏1,*, 马力1, 李文龙2   

  1. 1. 北京航空航天大学宇航学院, 北京 100191
    2. 上海卫星工程研究所, 上海 201109
  • 收稿日期:2023-11-27 出版日期:2024-09-25 发布日期:2024-10-22
  • 通讯作者: 师鹏
  • 作者简介:陈宇(1999—), 男, 硕士, 主要研究方向为体系效能评估
    师鹏(1981—), 男, 副教授, 博士, 主要研究方向为航天动力学与控制
    马力(1999—), 男, 硕士, 主要研究方向为体系效能评估
    李文龙(1988—), 男, 高级工程师, 博士, 主要研究方向为卫星总体设计、轨道与姿态控制
  • 基金资助:
    上海航天科技创新基金项目(SAST2022-050)

Modeling and effectiveness evaluation method of space-based information support system

Yu CHEN1, Peng SHI1,*, Li MA1, Wenlong LI2   

  1. 1. School of Astronautics, Beihang University, Beijing 100191, China
    2. Shanghai Institute of Satellite Engineering, Shanghai 201109, China
  • Received:2023-11-27 Online:2024-09-25 Published:2024-10-22
  • Contact: Peng SHI

摘要:

针对天基信息支援体系效能评估中存在的主观性强与复杂性高的问题, 提出一种基于投影梯度神经网络的天基信息支援体系效能评估方法。首先, 基于国防部体系框架(Department of Defense Architecture Framework, DoDAF)视图产品与包以德循环(observation, orientation, decision, action, OODA)梳理体系作战流程, 进而建立评估指标体系, 并基于离散事件仿真生成效能评估数据样本。然后, 基于Rosen-反向传播(back propagation, BP)神经网络构建效能评估代理模型, 并通过对权重参数的限制来解决在效益型指标下评估模型难以解释的问题。最后, 对仿真样本进行评估模型验证试验, 结果表明所提方法在天基信息支援体系效能评估中相较于传统BP神经网络计算性能提升超过50%, 能够为天基信息支援体系效能评估提供技术支撑。

关键词: 天基信息支援体系, 神经网络, 投影梯度法, 效能评估

Abstract:

Aiming at the problem of strong subjectivity and high complexity in the effectiveness evaluation of space-based information support system, a projection gradient neural network-based effectiveness evaluation method for space-based information support system is proposed. Firstly, based on the Department of Defense Architecture Framework (DoDAF) optical products and observation-orientation-decision-action (OODA) loop are used to sort out the system operational process, and then the evaluation index system is established, and the data samples for effectiveness evaluation are generated based on the discrete-event simulation. Then, the effectiveness evaluation agent model is constructed based on the Rosen-back propagation (BP) neural network, and the restriction of the weight parameter is used to solve the problem that the evaluation model is difficult to be interpreted under the efficiency-type indexes. Finally, a validation test of the evaluation model is conducted on the simulation samples, and the results show that the proposed method can improve the computational performance by more than 50% compared with the traditional BP neural network in the effectiveness evaluation of space-based information support systems, which can provide technical support for the evaluation of the effectiveness of space-based information support systems.

Key words: space-based information support system, neural network, projection gradient method, effectiveness evaluation

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