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SCIENCE CHINA Information Sciences
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SCIENCE CHINA Information Sciences, Volume 62 , Issue 1 : 010202(2019) https://doi.org/10.1007/s11432-018-9581-y

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

Yingrong YU , Siting PENG , Xiwang DONG *, Qingdong LI , Zhang REN
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  • ReceivedJun 27, 2018
  • AcceptedJul 19, 2018
  • PublishedDec 19, 2018
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Abstract


Acknowledgment

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).


References

[1] Sobhani B, Paolini E, Giorgetti A. Target Tracking for UWB Multistatic Radar Sensor Networks. IEEE J Sel Top Signal Process, 2014, 8: 125-136 CrossRef ADS Google Scholar

[2] Wu K, Cai Z, Zhao J. Target Tracking Based on a Nonsingular Fast Terminal Sliding Mode Guidance Law by Fixed-Wing UAV. Appl Sci, 2017, 7: 333 CrossRef Google Scholar

[3] Front Cover. IEEE Senss J, 2017, 17: C1-C1 CrossRef Google Scholar

[4] Wang Y, Lin P, Hong Y. Distributed regression estimation with incomplete data in multi-agent networks. Sci China Inf Sci, 2018, 61: 092202 CrossRef Google Scholar

[5] Dong X, Yu B, Shi Z. Time-Varying Formation Control for Unmanned Aerial Vehicles: Theories and Applications. IEEE Trans Contr Syst Technol, 2015, 23: 340-348 CrossRef Google Scholar

[6] Dong X, Zhou Y, Ren Z. 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 CrossRef Google Scholar

[7] Dong X, Zhou Y, Ren Z. Time-varying formation control for unmanned aerial vehicles with switching interaction topologies. Control Eng Practice, 2016, 46: 26-36 CrossRef Google Scholar

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

[9] Li Z, Ren W, Liu X. 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 CrossRef Google Scholar

[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 CrossRef Google Scholar

[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 CrossRef PubMed Google Scholar

[12] Yu W, Li C, Yu X. Economic power dispatch in smart grids: a framework for distributed optimization and consensus dynamics. Sci China Inf Sci, 2018, 61: 012204 CrossRef Google Scholar

[13] Peng Z, Wang D, Wang H. Distributed cooperative tracking of uncertain nonlinear multi-agent systems with fast learning. Neurocomputing, 2014, 129: 494-503 CrossRef Google Scholar

[14] Qin J, Ma Q, Gao H. Fault-Tolerant Cooperative Tracking Control via Integral Sliding Mode Control Technique. IEEE/ASME Trans Mechatron, 2018, 23: 342-351 CrossRef Google Scholar

[15] Li J. Distributed cooperative tracking of multi-agent systems with actuator faults. Trans Institute Measurement Control, 2015, 37: 1041-1048 CrossRef Google Scholar

[16] Li W, Jia Y, Du J. Distributed Kalman consensus filter with intermittent observations. J Franklin Institute, 2015, 352: 3764-3781 CrossRef Google Scholar

[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] 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] Chen B, Ho D W C, Zhang W A. Networked Fusion Estimation With Bounded Noises. IEEE Trans Automat Contr, 2017, 62: 5415-5421 CrossRef Google Scholar

[20] Battistelli G, Chisci L, Mugnai G. Consensus-Based Linear and Nonlinear Filtering. IEEE Trans Automat Contr, 2015, 60: 1410-1415 CrossRef Google Scholar

[21] Huanshui Zhang , Xinmin Song , Ling Shi . Convergence and Mean Square Stability of Suboptimal Estimator for Systems With Measurement Packet Dropping. IEEE Trans Automat Contr, 2012, 57: 1248-1253 CrossRef Google Scholar

[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] Li W, Wei G, Han F. Weighted Average Consensus-Based Unscented Kalman Filtering.. IEEE Trans Cybern, 2016, 46: 558-567 CrossRef PubMed Google Scholar

[24] Battistelli G, Chisci L. Stability of consensus extended Kalman filter for distributed state estimation. Automatica, 2016, 68: 169-178 CrossRef Google Scholar

[25] Li L, Xia Y. Stochastic stability of the unscented Kalman filter with intermittent observations. Automatica, 2012, 48: 978-981 CrossRef Google Scholar

[26] Chen J, Sun J, Wang G. Stochastic stability of extended filtering for non-linear systems with measurement packet losses. IET Contr Theor Appl, 2013, 7: 2048-2055 CrossRef Google Scholar

[27] Reif K, Gunther S, Yaz E. Stochastic stability of the discrete-time extended Kalman filter. IEEE Trans Automat Contr, 1999, 44: 714-728 CrossRef Google Scholar