CLC number: TP391.4
On-line Access: 2024-08-30
Received: 2023-09-14
Revision Accepted: 2024-08-30
Crosschecked: 2023-12-27
Cited: 0
Clicked: 885
Citations: Bibtex RefMan EndNote GB/T7714
Yuru HU, Wangyan LI, Lifeng WU, Zhensheng YU. An attack-resilient distributed extended Kalman consensus filtering algorithm with applications to multi-UAV tracking problems[J]. Frontiers of Information Technology & Electronic Engineering, 2024, 25(8): 1110-1122.
@article{title="An attack-resilient distributed extended Kalman consensus filtering algorithm with applications to multi-UAV tracking problems",
author="Yuru HU, Wangyan LI, Lifeng WU, Zhensheng YU",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="25",
number="8",
pages="1110-1122",
year="2024",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2300621"
}
%0 Journal Article
%T An attack-resilient distributed extended Kalman consensus filtering algorithm with applications to multi-UAV tracking problems
%A Yuru HU
%A Wangyan LI
%A Lifeng WU
%A Zhensheng YU
%J Frontiers of Information Technology & Electronic Engineering
%V 25
%N 8
%P 1110-1122
%@ 2095-9184
%D 2024
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2300621
TY - JOUR
T1 - An attack-resilient distributed extended Kalman consensus filtering algorithm with applications to multi-UAV tracking problems
A1 - Yuru HU
A1 - Wangyan LI
A1 - Lifeng WU
A1 - Zhensheng YU
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 25
IS - 8
SP - 1110
EP - 1122
%@ 2095-9184
Y1 - 2024
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2300621
Abstract: This study investigates how the events of deception attacks are distributed during the fusion of multi-sensor nonlinear systems. First, a deception attack with limited energy (DALE) is introduced under the framework of distributed extended Kalman consensus filtering (DEKCF). Next, a hypothesis testing-based mechanism to detect the abnormal data generated by DALE, in the presence of the error term caused by the linearization of the nonlinear system, is established. Once the DALE is detected, a new rectification strategy can be triggered to recalibrate the abnormal data, restoring it to its normal state. Then, an attack-resilient DEKCF (AR-DEKCF) algorithm is proposed, and its fusion estimation errors are demonstrated to satisfy the mean square exponential boundedness performance, under appropriate conditions. Finally, the effectiveness of the AR-DEKCF algorithm is confirmed through simulations involving multi-unmanned aerial vehicle (multi-UAV) tracking problems.
[1]Arasaratnam I, Haykin S, 2009. Cubature Kalman filters. IEEE Trans Autom Contr, 54(6):1254-1269.
[2]Battistelli G, Chisci L, 2016. Stability of consensus extended Kalman filter for distributed state estimation. Automatica, 68:169-178.
[3]Battistelli G, Chisci L, Selvi D, 2018. A distributed Kalman filter with event-triggered communication and guaranteed stability. Automatica, 93:75-82.
[4]Chen B, Hu GQ, Ho DWC, et al., 2016. Distributed covariance intersection fusion estimation for cyber-physical systems with communication constraints. IEEE Trans Autom Contr, 61(12):4020-4026.
[5]Chen B, Ho DWC, Hu GQ, et al., 2018. Secure fusion estimation for bandwidth constrained cyber-physical systems under replay attacks. IEEE Trans Cybern, 48(6):1862-1876.
[6]Chen SQ, Ho DWC, 2023. Edge-based sender-receiver event-triggered schemes for distributed filtering. IEEE Trans Circ Syst I Reg Papers, 70(5):2143-2155.
[7]Chong MS, Wakaiki M, Hespanha JP, 2015. Observability of linear systems under adversarial attacks. Proc American Control Conf, p.2439-2444.
[8]D’Afflisio E, Braca P, Willett P, 2021. Malicious AIS spoofing and abnormal stealth deviations: a comprehensive statistical framework for maritime anomaly detection. IEEE Trans Aerosp Electron Syst, 57(4):2093-2108.
[9]Fawzi H, Tabuada P, Diggavi S, 2014. Secure estimation and control for cyber-physical systems under adversarial attacks. IEEE Trans Autom Contr, 59(6):1454-1467.
[10]Ge XH, Xiao SY, Han QL, et al., 2022. Dynamic event-triggered scheduling and platooning control co-design for automated vehicles over vehicular ad-hoc networks. IEEE/CAA J Autom Sin, 9(1):31-46.
[11]Ge XH, Han QL, Wu Q, et al., 2023. Resilient and safe platooning control of connected automated vehicles against intermittent denial-of-service attacks. IEEE/CAA J Autom Sin, 10(5):1234-1251.
[12]Ge XH, Han QL, Zhang XM, et al., 2024. Communication resource-efficient vehicle platooning control with various spacing policies. IEEE/CAA J Autom Sin, 11(2):362-376.
[13]Gong XL, Sun YH, 2021. An innovative distributed filter for airborne distributed position and orientation system. Aerosp Sci Technol, 119:107155.
[14]Han F, Wei GL, Ding DR, et al., 2017. Local condition based consensus filtering with stochastic nonlinearities and multiple missing measurements. IEEE Trans Autom Contr, 62(9):4784-4790.
[15]Han F, Wang ZD, Dong HL, et al., 2022. A local approach to distributed H∞-consensus state estimation over sensor networks under hybrid attacks: dynamic event-triggered scheme. IEEE Trans Signal Inform Process Netw, 8:556-570.
[16]Huang JH, Tang Y, Yang W, et al., 2020. Resilient consensus-based distributed filtering: convergence analysis under stealthy attacks. IEEE Trans Ind Inform, 16(7):4878-4888.
[17]Ju YM, Ding DR, He X, et al., 2022. Consensus control of multi-agent systems using fault-estimation-in-the-loop: dynamic event-triggered case. IEEE/CAA J Autom Sin, 9(8):1440-1451.
[18]Julier SJ, Uhlmann JK, 1997. A non-divergent estimation algorithm in the presence of unknown correlations. Proc American Control Conf, p.2369-2373.
[19]Kai X, Wei CL, Liu LD, 2010. Robust extended Kalman filtering for nonlinear systems with stochastic uncertainties. IEEE Trans Syst Man Cybern Part A Syst Hum, 40(2):399-405.
[20]Li WY, Yang FW, Wei GL, 2018. A novel observability Gramian-based fast covariance intersection rule. IEEE Signal Process Lett, 25(10):1570-1574.
[21]Li WY, Wang ZD, Ho DWC, et al., 2020. On boundedness of error covariances for Kalman consensus filtering problems. IEEE Trans Autom Contr, 65(6):2654-2661.
[22]Li YZ, Shi L, Chen TW, 2018. Detection against linear deception attacks on multi-sensor remote state estimation. IEEE Trans Contr Netw Syst, 5(3):846-856.
[23]Liu D, Zhao YB, Yuan ZQ, et al., 2020. Target tracking methods based on a signal-to-noise ratio model. Front Inform Technol Electron Eng, 21(12):1804-1814.
[24]Liu QY, Wang ZD, He X, et al., 2019. Event-based distributed filtering over Markovian switching topologies. IEEE Trans Autom Contr, 64(4):1595-1602.
[25]Ning BD, Han QL, Zuo ZY, et al., 2023. Fixed-time and prescribed-time consensus control of multiagent systems and its applications: a survey of recent trends and methodologies. IEEE Trans Ind Inform, 19(2):1121-1135.
[26]Reif K, Gunther S, Yaz E, et al., 1999. Stochastic stability of the discrete-time extended Kalman filter. IEEE Trans Autom Contr, 44(4):714-728.
[27]Ren HR, Cheng ZJ, Qin JH, et al., 2023. Deception attacks on event-triggered distributed consensus estimation for nonlinear systems. Automatica, 154:111100.
[28]Rezaei H, Ghorbani M, 2022. Event-triggered resilient distributed extended Kalman filter with consensus on estimation. Int J Robust Nonl Contr, 32(3):1303-1315.
[29]Ryu K, Back J, 2022. Consensus optimization approach for distributed Kalman filtering: performance recovery of centralized filtering with proofs.
[30]Shi DW, Chen TW, Shi L, 2014. An event-triggered approach to state estimation with multiple point- and set-valued measurements. Automatica, 50(6):1641-1648.
[31]Wan C, Huang FJ, 2023. Adversarial attack based on prediction-correction.
[32]Wang R, Li YH, Sun H, et al., 2021. Freshness constraints of an age of information based event-triggered Kalman consensus filter algorithm over a wireless sensor network. Front Inform Technol Electron Eng, 22(1):51-67.
[33]Wang S, Li YY, Qi GQ, et al., 2023. Diffusion nonlinear estimation and distributed UAV path optimization for target tracking with intermittent measurements and unknown cross-correlations. Drones, 7(7):473.
[34]Wei GL, Li WY, Ding DR, et al., 2020. Stability analysis of covariance intersection-based Kalman consensus filtering for time-varying systems. IEEE Trans Syst Man Cybern Syst, 50(11):4611-4622.
[35]Wu JF, Jia QS, Johansson KH, et al., 2013. Event-based sensor data scheduling: trade-off between communication rate and estimation quality. IEEE Trans Autom Contr, 58(4):1041-1046.
[36]Xiao L, Boyd SP, Lall S, 2005. A scheme for robust distributed sensor fusion based on average consensus. Proc Fourth Int Symp on Information Processing in Sensor Networks, p.63-70.
[37]Xiao SY, Ge XH, Han QL, et al., 2022. Dynamic event-triggered platooning control of automated vehicles under random communication topologies and various spacing policies. IEEE Trans Cybern, 52(11):11477-11490.
[38]Xie ML, Ding DR, Ge XH, et al., 2022. Distributed platooning control of automated vehicles subject to replay attacks based on proportional integral observers. IEEE/CAA J Autom Sin, early access.
[39]Yang FS, Liang XH, Guan XH, 2021. Resilient distributed economic dispatch of a cyber-power system under DoS attack. Front Inform Technol Electron Eng, 22(1):40-50.
[40]Yang W, Lei L, Yang C, 2017. Event-based distributed state estimation under deception attack. Neurocomputing, 270:145-151.
[41]Yang W, Zhang Y, Chen GR, et al., 2019. Distributed filtering under false data injection attacks. Automatica, 102:34-44.
[42]Zhang XM, Han QL, Ge XH, et al., 2023. Sampled-data control systems with non-uniform sampling: a survey of methods and trends. Annu Rev Contr, 55:70-91.
[43]Zhang ZH, Liu D, Deng C, et al., 2020. A dynamic event-triggered resilient control approach to cyber-physical systems under asynchronous DoS attacks. Inform Sci, 519:260-272.
Open peer comments: Debate/Discuss/Question/Opinion
<1>