计算机科学 ›› 2021, Vol. 48 ›› Issue (5): 308-312.doi: 10.11896/jsjkx.200300117

• 信息安全 • 上一篇    下一篇

有适应力的分布式状态估计方法

高枫越1, 王琰2, 朱铁兰3   

  1. 1 陆军工程大学通信工程学院 南京210007
    2 军事科学院系统工程研究院 北京100141
    3 96125部队 沈阳110000
  • 收稿日期:2020-03-19 修回日期:2020-08-06 出版日期:2021-05-15 发布日期:2021-05-09
  • 通讯作者: 高枫越(350471891@qq.com)

Resilient Distributed State Estimation Algorithm

GAO Feng-yue1, WANG Yan2, ZHU Tie-lan3   

  1. 1 College of Communications Engineering,PLA University of Army Engineering,Nanjing 210007,China
    2 System Engineering Research Institute,Academy of Military Sciences PLA,Beijing 100141,China
    3 Unit 96125,Shenyang 110000,China
  • Received:2020-03-19 Revised:2020-08-06 Online:2021-05-15 Published:2021-05-09
  • About author:GAO Feng-yue,born in 1987,Ph.D candidate.His main research interests include cooperative communications,network coding and channel coding.

摘要: 为提高智能体系统对攻击的免疫力,研究了测量攻击下的适应力分布式状态估计方法。每个智能体对系统状态进行连续的本地线性测量。由于不同智能体的本地测量模型相互异构,对系统状态可能不具有本地可观测性,且攻击者能够操控部分智能体的测量数据,随意改变其测量结果。而智能体的目标是协同处理本地测量数据,并正确估计出未知的系统状态。因此,该问题的挑战在于在不对真实测量数据和恶意智能体的测量数据进行分辨时,如何设计算法估计得到真实的系统状态。为了解决这个问题,设计了适应性分布式最大后验概率估计算法。在该算法中,只要恶意智能体的数量小于某个特定值,所有智能体都能够收敛到系统状态。首先,根据卡尔曼滤波给出集中式最大后验概率(Maximum A Posteriori,MAP)估计方法,并与分布式一致性结合,进而得到分布式最大后验概率估计方法。然后,考虑到测量攻击,从估计一致性的角度,利用自适应饱和度增益设计了适应性分布式最大后验概率估计方法。最后,通过仿真实验验证算法的有效性。

关键词: 多智能体系统, 分布式状态估计, 卡尔曼滤波, 适应性估计, 一致性滤波, 最大后验概率

Abstract: In order to improve the immunity of multi-agent system against attack,resilient distributed state estimation under measurement attacks is studied.Each agent makes successive local linear measurements of the system state.The local measurement models are heterogeneous across agents and may be locally unobservable for the system state.An adversary compromises some of the measurement streams and changes their values arbitrarily.The agents' goal is to cooperate with their local measurements and estimate the value of the system state correctly.The challenge of this problem is how to design an algorithm to estimate the real system state without distinguishing the real measurements from the measurements of malicious agents.In order to solve this problem,an adaptive distributed maximum a posteriori probability estimation algorithm is designed.As long as the number of compromised measurement streams is lower than a particular bound,all of the agents' local estimates,including malicious agents' local estimates,can converge to the true system state.Firstly,a centralized maximum a posteriori (MAP) estimation method is proposed based on Kalman filter.Combining a centralized MAP estimation with distributed consensus protocol,a distributed MAP estimation method is derived.Then,considering the measurement attack and analyzing the consistency of distributed estimates,a resilient distributed MAP estimation method is designed by exploiting the saturating adaptive gain,which gives a small gain if the deviation from the practical measurement resulting from the attacks is too large.At last,Numerical simulations are provided to evaluate the effectiveness of the proposed algorithm against measurement attacks.

Key words: Consensus filter, Distributed state estimation, Kalman filter, Maximum a posteriori, Multi-agent system, Resilient estimation

中图分类号: 

  • TP13
[1]RASTGAR F,RAHMANI M.Consensus-based distributed robust filtering for multisensory systems with stochastic uncertainties [J].IEEE Sensors Journal,2018,18:7611-7618.
[2]DESHMUKH R,KWON C,HWANG I.Optimal Discrete-Time Kalman Consensus Filter [C]//Proceedings of the 2017 American Control Conference (ACC).Seattle,WA,USA,2017:5801-5806.
[3]AMINIOMAM M,TORKAMANI-AZAR F,GHORASHI S A.Generalised Kalman-consensus filter[J].IET Signal Processing,2017,11(5):495-502.
[4]ZHANG S,LIU W Q,ZHAO N.Research of Consensus inMulti-agent Systems on Complex Network[J].Computer Science,2019,46(4):95-99.
[5]PASQUALETTI F,DÖRFLER F,BULLO F.Attack detection and identification in cyber-physical systems[J].IEEE transactions on automatic control,2013,58(11):2715-2729.
[6]LAMPORT L,SHOSTAK R,PEASE M.The Byzantine Gene-rals Problem[J].ACM Transactions on Programming Languages and Systems,1982,4(3):382-401.
[7]DOLEV D,LYNCH N A,PINTER S S,et al.Reaching approximate agreement in the presence of faults[J].Journal of the ACM (JACM),1986,33(3):499-516.
[8]LEBLANC H J,ZHANG H,KOUTSOUKOS X,et al.Resilient asymptotic consensus in robust networks[J].IEEE Journal on Selected Areas in Communications,2013,31(4):766-781.
[9]CHEN Y,KAR S,MOURA J M F.Resilient distributed estimation:Sensor attacks[J].IEEE Transactions on Automatic Control,2018,64(9):3772-3779.
[10]MITRA A,RICHARDS J A,BAGCHI S,et al.Resilient distri-buted state estimation with mobile agents:overcoming Byzantine adversaries,communication losses,and intermittent measurements[J].Autonomous Robots,2019,43(3):743-768.
[11]FORTI N,BATTISTELLI G,CHISCI L,et al.Distributed joint attack detection and secure state estimation[J].IEEE Transactions on Signal and Information Processing over Networks,2017,4(1):96-110.
[12]GUAN Y,GE X.Distributed attack detection and secure estimation of networked cyber-physical systems against false data injection attacks and jamming attacks[J].IEEE Transactions on Signal and Information Processing over Networks,2017,4(1):48-59.
[13]BOLLOBÁS B.Modern graph theory[M].Springer Science & Business Media,2013.
[14]OLFATI-SABER R,FAX J A,MURRAY R M.Consensus and cooperation in networked multi-agent systems[J].Proceedings of the IEEE,2007,95(1):215-233.
[15]WANG S,REN W.On the convergence conditions of distributed dynamic state estimation using sensor networks:A unified framework[J].IEEE Transactions on Control Systems Techno-logy,2017,26(4):1300-1316.
[16]BATTISTELLI G,CHISCI L.Kullback-Leibler average,con-sensus on probability densities,and distributed state estimation with guaranteed stability[J].Automatica,2014,50(3):707-718.
[17]HE X,REN X,SANDBERG H,et al.Secure distributed filtering for unstable dynamics under compromised observations[J].ar-Xiv:1903.07345,2019.
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