Computer Science ›› 2021, Vol. 48 ›› Issue (5): 308-312.doi: 10.11896/jsjkx.200300117

• Information Security • Previous Articles     Next Articles

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.

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

CLC Number: 

  • 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.
[1] SHI Dian-xi, LIU Cong, SHE Fu-jiang, ZHANG Yong-jun. Cooperation Localization Method Based on Location Confidence of Multi-UAV in GPS-deniedEnvironment [J]. Computer Science, 2022, 49(4): 302-311.
[2] WANG Bing-zhou, WANG Hui-bin, SHEN Jie, ZHANG Li-li. FastSLAM Algorithm Based on Adaptive Fading Unscented Kalman Filter [J]. Computer Science, 2020, 47(9): 213-218.
[3] LI Li. Classification Algorithm of Distributed Data Mining Based on Judgment Aggregation [J]. Computer Science, 2020, 47(6A): 450-456.
[4] MA Hong. Fusion Localization Algorithm of Visual Aided BDS Mobile Robot Based on 5G [J]. Computer Science, 2020, 47(6A): 631-633.
[5] XU Zi-xi, MAO Xin-jun, YANG Yi, LU Yao. Modeling and Simulation of Q&A Community and Its Incentive Mechanism [J]. Computer Science, 2020, 47(6): 32-37.
[6] FENG An-qi, QIAN Li-ping, OUYANG Jin-yuan, WU Yuan. Vehicular Networking Enabled Vehicle State Prediction with Two-level Quantized AdaptiveKalman Filtering [J]. Computer Science, 2020, 47(5): 230-235.
[7] WU Tian-tian,WANG Jie. Belief Coordination for Multi-agent System Based on Possibilistic Answer Set Programming [J]. Computer Science, 2020, 47(2): 201-205.
[8] ZHANG Liang-cheng, WANG Yun-feng. Dynamic Adaptive Multi-radar Tracks Weighted Fusion Method [J]. Computer Science, 2020, 47(11A): 321-326.
[9] HUANG Ting-ting, FENG Feng. Study on Optimization of Heterogeneous Data Fusion Model in Wireless Sensor Network [J]. Computer Science, 2020, 47(11A): 339-344.
[10] WANG Hong-xia,XU Ying-jie,ZHAO Yun-bo,ZHANG Wen-an. PET Image Reconstruction Based on Unbiased Linear Optimal Estimation [J]. Computer Science, 2020, 47(1): 165-169.
[11] DU Wei, DING Shi-fei. Overview on Multi-agent Reinforcement Learning [J]. Computer Science, 2019, 46(8): 1-8.
[12] ZHANG Sen, LIU Wen-qi, ZHAO Ning. Research of Consensus in Multi-agent Systems on Complex Network [J]. Computer Science, 2019, 46(4): 95-99.
[13] FENG An-qi, QIAN Li-ping, HUANG Yu-pin, WU Yuan. RFID Data-driven Vehicle Speed Prediction Using Adaptive Kalman Filter [J]. Computer Science, 2019, 46(4): 100-105.
[14] JIANG Zhi-ying, LIU Ri-sheng. Deep Convolutional Prior Guided Robust Image Separation Method and Its Applications [J]. Computer Science, 2019, 46(3): 119-124.
[15] MA Lin-hong, CHEN Ting-wei, HAO Ming, ZHANG Lei. Bus Travel Time Prediction Algorithm Based on Multi-line Information Fusion [J]. Computer Science, 2019, 46(11): 222-227.
Full text



No Suggested Reading articles found!