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恶意攻击下基于分布式稀疏优化的安全状态估计

张岱峰 段海滨

张岱峰, 段海滨. 恶意攻击下基于分布式稀疏优化的安全状态估计. 自动化学报, 2021, 47(4): 813−824 doi: 10.16383/j.aas.c200276
引用本文: 张岱峰, 段海滨. 恶意攻击下基于分布式稀疏优化的安全状态估计. 自动化学报, 2021, 47(4): 813−824 doi: 10.16383/j.aas.c200276
Zhang Dai-Feng, Duan Hai-Bin. Secure state estimation based on distributed sparse optimization under malicious attacks. Acta Automatica Sinica, 2021, 47(4): 813−824 doi: 10.16383/j.aas.c200276
Citation: Zhang Dai-Feng, Duan Hai-Bin. Secure state estimation based on distributed sparse optimization under malicious attacks. Acta Automatica Sinica, 2021, 47(4): 813−824 doi: 10.16383/j.aas.c200276

恶意攻击下基于分布式稀疏优化的安全状态估计

doi: 10.16383/j.aas.c200276
基金项目: 国家自然科学基金(U20B2071, 91948204, U1913602, U19B2033), 科技创新2030“新一代人工智能”重大项目(2018AAA0102303), 航空科学基金(20185851022)资助
详细信息
    作者简介:

    张岱峰:北京航空航天大学自动化科学与电气工程学院博士研究生. 2013年于合肥工业大学获得学士学位, 2016年于北京航空航天大学获得硕士学位. 主要研究方向为多智能体协调控制与决策. E-mail: zdfskh@163.com

    段海滨:北京航空航天大学自动化科学与电气工程学院长聘教授. 2005年于南京航空航天大学获博士学位, 分别于2007年、2011年在新加坡国立大学、韩国水源大学从事访问学者研究. 主要研究方向为仿生智能, 无人机自主控制. 本文通信作者. E-mail: hbduan@buaa.edu.cn

Secure State Estimation Based on Distributed Sparse Optimization Under Malicious Attacks

Funds: Supported by National Natural Science Foundation of China (U20B2071, 91948204, U1913602, U19B2033), Science and Technology Innovation 2030-Key Project of “New Generation Artificial Intelligence” (2018AAA0102303), and Aeronautical Science Foundation of China (20185851022)
More Information
    Author Bio:

    ZHANG Dai-Feng Ph. D. candidate at the School of Automation Science and Electrical Engineering, Beihang University. He received his bachelor degree from Hefei University of Technology in 2013, and the master degree from Beihang University in 2016. His main research interest is the cooperative control and decision of multi-agent systems

    DUAN Hai-Bin Long-term professor at the School of Automation Science and Electrical Engineering, Beihang University. He received his Ph. D. degree from Nanjing University of Aeronautics and Astronautics (NUAA) in 2005. He was an academic visitor of National University of Singapore (NUS) in 2007, a senior visiting scholar of the University of Suwon (USW) of South Korea in 2011. His research interest covers bio-inspired intelligence, and autonomous control of unmanned aerial vehicles. Corresponding author of this paper

  • 摘要: 恶意生成的量测攻击信号是导致信息物理系统(Cyber-physical system, CPS)探测失效的主要原因, 如何有效削弱其影响是实现精准探测、跟踪与感知的关键问题. 分布式传感器网络(Distributed sensor network, DSN)依靠多传感器协作与并行处理突破单一监测节点的任务包线, 能够显著提升探测系统跟踪精度与可靠性. 首先, 依据压缩感知理论, 将单一节点的目标运动状态估计建模为一种基于l0范数最小化的稀疏优化问题, 采用正交匹配追踪法(Orthogonal matching pursuit, OMP)重构量测攻击信号, 以克服采用凸优化算法求解易陷入局部最优的缺陷. 通过卡尔曼滤波量测更新抵消攻击信号影响, 恢复目标运动的真实状态. 其次, 针对错误注入攻击等复杂量测攻击形式, 基于势博弈理论, 提出一种分布式稀疏优化安全状态估计方法, 利用多传感器节点信息交互与协作提升探测与跟踪的稳定性. 仿真结果表明, 所提方法在分布式传感器网络协作抵抗恶意攻击方面具有优越性.
  • 图  1  分布式稀疏优化安全状态估计流程图

    Fig.  1  Flowchart of secure state estimation algorithm based on distributed sparse optimizations

    图  2  DSN网络通信拓扑结构

    Fig.  2  Communication topology of DSN nodes

    图  3  测试算法对目标轨迹的跟踪效果对比

    Fig.  3  Comparison of trajectory tracking by the candidate algorithms

    图  4  测试算法对目标X轴位置的跟踪误差对比

    Fig.  4  Comparison of X-position estimation errors by the candidate algorithms

    图  5  测试算法对目标X轴速度的跟踪误差对比

    Fig.  5  Comparison of X-velocity estimation errors by the candidate algorithms

    图  6  算法A1中探测节点的融合策略选择

    Fig.  6  Fusion decision making by the nodes in A1

    表  1  DSN遭受的分时段攻击列表

    Table  1  Time sharing attacks of DSN detection nodes

    节点时段
    0 s ~ 3 s3 s ~ 6 s6 s ~ 9 s9 s ~ 10 s
    1I类
    2I类I类
    3II类II类
    4II类II类
    5I类I类
    下载: 导出CSV

    表  2  5种算法对X轴位置的均方估计误差比较 (m)

    Table  2  Comparison of mean square errors (meters) of position estimations in X-axis by the five algorithms

    A1 A2 A3 A4 A5
    节点 1 0.0724 0.0238 28.6083 0.0302 3.6060
    节点 2 0.0150 7.2185 33.0688 8.3714 3.5778
    节点 3 0.0724 0.2550 29.0511 0.3338 3.6056
    节点 4 1.3807 40.6553 29.5093 39.4624 3.5778
    节点 5 0.0054 0.4213 31.6166 0.1078 3.5785
    下载: 导出CSV
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  • 收稿日期:  2020-05-02
  • 录用日期:  2020-08-14
  • 修回日期:  2020-07-03
  • 刊出日期:  2021-04-23

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