UIF-based cooperative tracking method for multi-agent systems with sensor faults


For maneuvering target tracking with sensor faults, consensus-based distributed state estimation problems are studied herein. The communication status of the nonlinear system composed of multiple agents is described using the graph theory. Considering the impacts caused by sensor failures on measurement equations, a weighted average consensus-based unscented information filter (UIF) algorithm is proposed to improve tracking accuracy. Moreover, the estimation error for the investigated nonlinear system has been analyzed based on the stochastic boundedness theory to evaluate the proposed algorithm’s performance and feasibility. Finally, simulation results are presented to assert the validity of the method.

This is a preview of subscription content, access via your institution.


  1. 1

    Sobhani B, Paolini E, Giorgetti A, et al. Target tracking for UWB multistatic radar sensor networks. IEEE J Sel Top Signal Process, 2014, 8: 125–136

    Article  Google Scholar 

  2. 2

    Wu K, Cai Z, Zhao J, et al. Target tracking based on a nonsingular fast terminal sliding mode guidance law by fixed-wing UAV. Appl Sci, 2017, 7: 333

    Article  Google Scholar 

  3. 3

    Chen H Y, Zhang S L, Liu M Q, et al. An artificial measurements-based adaptive filter for energy-efficient target tracking via underwater wireless sensor networks. Sensors, 2017, 17: 1–19

    Article  Google Scholar 

  4. 4

    Wang Y H, Lin P, Hong Y G. Distributed regression estimation with incomplete data in multi-agent networks. Sci China Inf Sci, 2018, 61: 092202

    MathSciNet  Article  Google Scholar 

  5. 5

    Dong X, Yu B, Shi Z, et al. Time-varying formation control for unmanned aerial vehicles: theories and applications. IEEE Trans Control Syst Technol, 2015, 23: 340–348

    Article  Google Scholar 

  6. 6

    Dong X W, Zhou Y, Ren Z, et al. Time-varying formation tracking for second-order multi-agent systems subjected to switching topologies with application to quadrotor formation flying. IEEE Trans Ind Electron, 2017, 64: 5014–5024

    Article  Google Scholar 

  7. 7

    Dong X W, Zhou Y, Ren Z, et al. Time-varying formation control for unmanned aerial vehicles with switching interaction topologies. Control Eng Practic, 2016, 46: 26–36

    Article  Google Scholar 

  8. 8

    Fang H, Shang C S, Chen J. An optimization-based shared control framework with applications in multi-robot systems. Sci China Inf Sci, 2018, 61: 014201

    MathSciNet  Article  Google Scholar 

  9. 9

    Li Z K, Ren W, Liu X D, et al. Distributed containment control of multi-agent systems with general linear dynamics in the presence of multiple leaders. Int J Robust Nonlinear Control, 2013, 23: 534–547

    MathSciNet  Article  Google Scholar 

  10. 10

    Blackman S S. Abstracts of previous tutorials in this series: multiple hypothesis tracking for multiple target tracking. IEEE Aerosp Electron Syst Mag, 2016, 31: 90–96

    Article  Google Scholar 

  11. 11

    Milan A, Schindler K, Roth S. Multi-target tracking by discrete-continuous energy minimization. IEEE Trans Pattern Anal Mach Intell, 2016, 38: 2054–2068

    Article  Google Scholar 

  12. 12

    Yu W W, Li C J, Yu X H, et al. Economic power dispatch in smart grids: a framework for distributed optimization and consensus dynamics. Sci China Inf Sci, 2018, 61: 012204

    Article  Google Scholar 

  13. 13

    Peng Z H, Wang D, Wang H, et al. Distributed cooperative tracking of uncertain nonlinear multi-agent systems with fast learning. Neurocomputing, 2014, 129: 494–503

    Article  Google Scholar 

  14. 14

    Qin J H, Ma Q C, Gao H J, et al. Fault-tolerant cooperative tracking control via integral sliding mode control technique. IEEE/ASME Trans Mechatron, 2018, 23: 342–351

    Article  Google Scholar 

  15. 15

    Li J Z. Distributed cooperative tracking of multi-agent systems with actuator faults. Trans Inst Meas Control, 2015, 37: 1041–1048

    Article  Google Scholar 

  16. 16

    Li W L, Jia Y M, Du J P. Distributed Kalman consensus filter with intermittent observations. J Franklin Inst, 2015, 352: 3764–3781

    MathSciNet  Article  Google Scholar 

  17. 17

    Tan Q K, Dong X W, Liu F, et al. Weighted average consensus-based cubature information filtering for mobile sensor networks with intermittent observations. In: Proceedings of Chinese Control Conference, Dalian, 2017. 8946–8951

    Google Scholar 

  18. 18

    Ding J L, Xiao J, Zhang Y. Distributed algorithm-based CKF and its applications to target tracking. Control Decis, 2015, 30: 296–302

    Google Scholar 

  19. 19

    Chen B, Ho D W C, Zhang W A, et al. Networked fusion estimation with bounded noises. IEEE Trans Autom Control, 2017, 62: 5415–5421

    MathSciNet  Article  Google Scholar 

  20. 20

    Battistelli G, Chisci L, Mugnai G, et al. Consensus-based linear and nonlinear filtering. IEEE Trans Autom Control, 2015, 60: 1410–1415

    MathSciNet  Article  Google Scholar 

  21. 21

    Zhang H S, Song X X, Shi L. Convergence and mean square stability of suboptimal estimator for systems with measurement packet dropping. IEEE Trans Autom Control, 2012, 57: 1248–1253

    MathSciNet  Article  Google Scholar 

  22. 22

    Du Y K, Ju H Y, Yong H K, et al. Distributed information fusion filter with intermittent observations. In: Proceedings of the Conference on Information Fusion, Edinburgh, 2010

    Google Scholar 

  23. 23

    Li W Y, Wei G L, Han F, et al. Weighted average consensus-based unscented kalman filtering. IEEE Trans Cybern, 2016, 46: 558–567

    Article  Google Scholar 

  24. 24

    Battistelli G, Chisci L. Stability of consensus extended Kalman filter for distributed state estimation. Automatica, 2016, 68: 169–178

    MathSciNet  Article  Google Scholar 

  25. 25

    Li L, Xia Y Q. Stochastic stability of the unscented Kalman filter with intermittent observations. Automatica, 2012, 48: 978–981

    MathSciNet  Article  Google Scholar 

  26. 26

    Chen J, Sun J, Wang G. Stochastic stability of extended filtering for non-linear systems with measurement packet losses. IET Control Theory Appl, 2013, 7: 2048–2055

    MathSciNet  Article  Google Scholar 

  27. 27

    Reif K, Gunther S, Yaz E, et al. Stochastic stability of the discrete-time extended Kalman filter. IEEE Trans Autom Control, 1999, 44: 714–728

    MathSciNet  Article  Google Scholar 

Download references


This work was supported by National Natural Science Foundation of China (Grant Nos. 61873011, 61803014, 61503009, 61333011), Beijing Natural Science Foundation (Grant No. 4182035), Young Elite Scientists Sponsorship Program by CAST (Grant No. 2017QNRC001), Aeronautical Science Foundation of China (Grant Nos. 2016ZA51005, 20170151001), Special Research Project of Chinese Civil Aircraft, State Key Laboratory of Intelligent Control and Decision of Complex Systems, and Fundamental Research Funds for the Central Universities (Grant No. YWF-18-BJ-Y-73).

Author information



Corresponding author

Correspondence to Xiwang Dong.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Yu, Y., Peng, S., Dong, X. et al. UIF-based cooperative tracking method for multi-agent systems with sensor faults. Sci. China Inf. Sci. 62, 10202 (2019). https://doi.org/10.1007/s11432-018-9581-y

Download citation


  • unscented information filter
  • consensus theory
  • cooperative tracking
  • multi-agent system
  • stochastic boundedness