2504000027
  • Open Access
  • Survey/Review Study
A Survey on Recent Advances in Distributed Filtering over Sensor Networks Subject to Communication Constraints
  • Yu-Ang Wang 1, 2,   
  • Bo Shen 1, 2,   
  • Lei Zou 1, 2,   
  • Qing-Long Han 3, *

Received: 13 Dec 2022 | Accepted: 31 Jan 2023 | Published: 23 Jun 2023

Abstract

The sensor network (SN) has long been an ongoing research topic with many successful applications in a wide range of fields. Lying in the core part of this paper is the distributed filtering problem over SNs that has been gaining growing research interests. We aim to provide a timely survey on recent advances in the distributed filtering problems over SNs subject to communication constraints. First, some basic knowledge concerning the distributed filter design issue is provided in terms of background introduction and mathematical descriptions. Then, some frequently encountered network-induced complexities resulting from communication constraints are comprehensively reviewed. Subsequently, the latest research progress of the distributed filtering schemes is discussed in detail for various systems over SNs with different performance specifications. Furthermore, practical applications of the distributed filtering methods are presented that include target tracking and distributed generation. Finally, concluding remarks are given followed by possible future research directions.

Graphical Abstract

References 

  • 1.
    Bianchi, P.; Jakubowicz, J.; Roueff, F. Linear precoders for the detection of a Gaussian process in wireless sensors networks. IEEE Trans. Signal Process., 2011, 59: 882−894.
  • 2.
    Kokiopoulou, E.; Frossard, P. Distributed classification of multiple observation sets by consensus. IEEE Trans. Signal Process., 2011, 59: 104−114.
  • 3.
    Ding, D.R.; Wang, Z.D.; Han, Q.L.; et al. Recursive secure filtering over Gilbert-Elliott channels in sensor networks: The distributed case. IEEE Trans. Signal Inf. Proc. Netw., 2021, 7: 75−86.
  • 4.
    Zhu, M.Z.; Chen, Y.; Kong, Y.G.; et al. Distributed filtering for Markov jump systems with randomly occurring one-sided Lipschitz nonlinearities under round-robin scheduling. Neurocomputing, 2020, 417: 396−405.
  • 5.
    Wang, F.; Wang, Z.D.; Liang, J.L.; et al. Recursive distributed filtering for two-dimensional shift-varying systems over sensor networks under stochastic communication protocols. Automatica, 2020, 115: 108865.
  • 6.
    Yang, H.J.; Li, H.; Xia, Y.Q.; et al. Distributed Kalman filtering over sensor networks with transmission delays. IEEE Trans. Cybern., 2021, 51: 5511−5521.
  • 7.
    Ding, D.R.; Wang, Z.D.; Ho, D.W.C.; et al. Distributed recursive filtering for stochastic systems under uniform quantizations and deception attacks through sensor networks. Automatica, 2017, 78: 231−240.
  • 8.
    Zhang, D.; Xu, Z.H.; Karimi, H.R.; et al. Distributed filtering for switched linear systems with sensor networks in presence of packet dropouts and quantization. IEEE Trans. Circuits Syst. I Reg. Papers, 2017, 64: 2783−2796.
  • 9.
    Shen, B.; Wang, Z.D.; Huang, Y.S. Distributed H∞-consensus filtering in sensor networks with multiple missing measurements: The finite-horizon case. Automatica, 2010, 46: 1682−1688.
  • 10.
    Dong, H.L.; Wang, Z.D.; Gao, H.J. Distributed filtering for a class of time-varying systems over sensor networks with quantization errors and successive packet dropouts. IEEE Trans. Signal Process., 2012, 60: 3164−3173.
  • 11.
    Ma, L.F.; Wang, Z.D.; Lam, H.K.; et al. Distributed event-based set-membership filtering for a class of nonlinear systems with sensor saturations over sensor networks. IEEE Trans. Cybern., 2017, 47: 3772−3783.
  • 12.
    Ge, X.H.; Han, Q.L.; Wang, Z.D. A dynamic event-triggered transmission scheme for distributed set-membership estimation over wireless sensor networks. IEEE Trans. Cybern., 2019, 49: 171−183.
  • 13.
    Ding, D.R.; Wang, Z.D.; Han, Q.L. A set-membership approach to event-triggered filtering for general nonlinear systems over sensor networks. IEEE Trans. Autom. Control, 2020, 65: 1792−1799.
  • 14.
    Liu, Q.Y.; Wang, Z.D.; He, X.; et al. Event-based recursive distributed filtering over wireless sensor networks. IEEE Trans. Autom. Control, 2015, 60: 2470−2475.
  • 15.
    Li, Q.; Wang, Z.D.; Shen, B.; et al. A resilient approach to recursive distributed filtering for multirate systems over sensor networks with time-correlated fading channels. IEEE Trans. Signal Inf. Proc. Netw., 2021, 7: 636−647.
  • 16.
    Shen, B.; Wang, Z.D.; Wang, D.; et al. Distributed state-saturated recursive filtering over sensor networks under round-robin protocol. IEEE Trans. Cybern., 2020, 50: 3605−3615.
  • 17.
    Ma, L.F.; Wang, Z.D.; Han, Q.L.; et al. Variance-constrained distributed filtering for time-varying systems with multiplicative noises and deception attacks over sensor networks. IEEE Sensors J., 2017, 17: 2279−2288.
  • 18.
    Vázquez, M.A.; Míguez, J. A robust scheme for distributed particle filtering in wireless sensors networks. Signal Process., 2017, 131: 190−201.
  • 19.
    Hlinka, O.; Slučiak, O.; Hlawatsch, F.; et al. Likelihood consensus and its application to distributed particle filtering. IEEE Trans. Signal Process., 2012, 60: 4334−4349.
  • 20.
    Ghirmai, T. Distributed particle filter for target tracking: With reduced sensor communications. Sensors, 2016, 16: 1454.
  • 21.
    Yan, H.C.; Qian, F.F.; Yang, F.W.; et al. H∞ filtering for nonlinear networked systems with randomly occurring distributed delays, missing measurements and sensor saturation. Inf. Sci. 2016, 370–371, 772–782. doi: 10.1016/j.ins.2015.09.027
  • 22.
    Jin, H.; Sun, S.L. Distributed filtering for sensor networks with fading measurements and compensations for transmission delays and losses. Signal Process., 2022, 190: 108306.
  • 23.
    Feng, S.Y.; Yu, H.; Jia, C.Q.; et al. Joint state and fault estimation for nonlinear complex networks with mixed time-delays and uncertain inner coupling: Non-fragile recursive method. Syst. Sci. Control Eng., 2022, 10: 603−615.
  • 24.
    Li, Z.H.; Hu, J.; Li, J.X. Distributed filtering for delayed nonlinear system with random sensor saturation: A dynamic event-triggered approach. Syst. Sci. Control Eng., 2021, 9: 440−454.
  • 25.
    Liu, Q.Y.; Wang, Z.D.; He, X.; et al. Event-based distributed filtering with stochastic measurement fading. IEEE Trans. Ind. Inf., 2015, 11: 1643−1652.
  • 26.
    Xu, Y.; Lu, R.Q.; Shi, P.; et al. Finite-time distributed state estimation over sensor networks with round-robin protocol and fading channels. IEEE Trans. Cybern., 2018, 48: 336−345.
  • 27.
    Zhu, Y.Z.; Zhang, L.X.; Zheng, W.X. Distributed H∞ filtering for a class of discrete-time Markov jump Lur’e systems with redundant channels. IEEE Trans. Industr. Electron., 2016, 63: 1876−1885.
  • 28.
    Zhu, K.Q.; Wang, Z.D.; Han, Q.L.; et al. Distributed set-membership fusion filtering for nonlinear 2-D systems over sensor networks: An encoding-decoding scheme. IEEE Trans. Cybern., 2023, 53: 416−427.
  • 29.
    Zhang, L.X.; Ning, Z.P.; Wang, Z.D. Distributed filtering for fuzzy time-delay systems with packet dropouts and redundant channels. IEEE Trans. Syst. Man Cybern. Syst., 2016, 46: 559−572.
  • 30.
    Yu, H.Y.; Zhuang, Y.; Wang, W. Distributed H∞ filtering in sensor networks with randomly occurred missing measurements and communication link failures. Inf. Sci., 2013, 222: 424−438.
  • 31.
    Wen, C.B.; Wang, Z.D.; Liu, Q.Y.; et al. Recursive distributed filtering for a class of state-saturated systems with fading measurements and quantization effects. IEEE Trans. Syst. Man Cybern. Syst., 2018, 48: 930−941.
  • 32.
    Liu, S.; Wang, Z.D.; Wei, G.L.; et al. Distributed set-membership filtering for multirate systems under the round-robin scheduling over sensor networks. IEEE Trans. Cybern., 2020, 50: 1910−1920.
  • 33.
    Hu, Z.B.; Hu, J.; Tan, H.L.; et al. Distributed resilient fusion filtering for nonlinear systems with random sensor delay under round-robin protocol. Int. J. Syst. Sci., 2022, 53: 2786−2799.
  • 34.
    Liu, K.; Guo, H.; Zhang, Q.R.; et al. Distributed secure filtering for discrete-time systems under round-robin protocol and deception attacks. IEEE Trans. Cybern., 2020, 50: 3571−3580.
  • 35.
    Ugrinovskii, V.; Fridman, E. A round-robin type protocol for distributed estimation with H∞ consensus. Syst. Control Lett., 2014, 69: 103−110.
  • 36.
    Ju, Y.M.; Wei, G.L.; Ding, D.R.; et al. A novel fault detection method under weighted try-once-discard scheduling over sensor networks. IEEE Trans. Control Netw. Syst., 2020, 7: 1489−1499.
  • 37.
    Li, X.; Wei, G.L.; Ding, D.R.; et al. Recursive filtering for time-varying discrete sequential systems subject to deception attacks: Weighted try-once-discard protocol. IEEE Trans. Syst. Man Cybern. Syst., 2022, 52: 3704−3713.
  • 38.
    Liu, S.; Zhao, X.X.; Tian, E.G.; et al. Distributed recursive filtering under random access protocols: A multirate strategy. Int. J. Robust Nonlinear Control, 2022, 32: 7132−7148.
  • 39.
    Wan, X.B.; Wang, Z.D.; Han, Q.L.; et al. Finite-time H∞ state estimation for discrete time-delayed genetic regulatory networks under stochastic communication protocols. IEEE Trans. Circuits Syst. I Reg. Papers, 2018, 65: 3481−3491.
  • 40.
    Han, F.; Song, Y.; Zhang, S.J.; et al. Local condition-based finite-horizon distributed H∞-consensus filtering for random parameter system with event-triggering protocols. Neurocomputing, 2017, 219: 221−231.
  • 41.
    Ge, X.H.; Han, Q.L. Distributed event-triggered H∞ filtering over sensor networks with communication delays. Inf. Sci., 2015, 291: 128−142.
  • 42.
    Zhu, S.Y.; Chen, C.L.; Li, W.S.; et al. Distributed optimal consensus filter for target tracking in heterogeneous sensor networks. IEEE Trans. Cybern., 2013, 43: 1963−1976.
  • 43.
    Millán, P.; Orihuela, L.; Vivas, C. et al. Distributed consensus-based estimation considering network induced delays and dropouts. Automatica, 2012, 48: 2726−2729.
  • 44.
    Olfati-Saber, R.; Jalalkamali, P. Coupled distributed estimation and control for mobile sensor networks. IEEE Trans. Autom. Control, 2012, 57: 2609−2614.
  • 45.
    Han, F.; Wei, G.L.; Ding, D.R.; et al. Local condition based consensus filtering with stochastic nonlinearities and multiple missing measurements. IEEE Trans. Autom. Control, 2017, 62: 4784−4790.
  • 46.
    Liang, J.L.; Wang, Z.D.; Liu, X.H. Distributed state estimation for discrete-time sensor networks with randomly varying nonlinearities and missing measurements. IEEE Trans. Neural Netw., 2011, 22: 486−496.
  • 47.
    Shen, B.; Wang, Z.D.; Liu, X.H. A stochastic sampled-data approach to distributed H∞ filtering in sensor networks. IEEE Trans. Circuits Syst. I Reg. Papers, 2011, 58: 2237−2246.
  • 48.
    Ding, D.R.; Wang, Z.D.; Dong, H.L.; et al. Distributed H∞ state estimation with stochastic parameters and nonlinearities through sensor networks: The finite-horizon case. Automatica, 2012, 48: 1575−1585.
  • 49.
    Liu, Q.Y.; Wang, Z.D.; He, X.; et al. A resilient approach to distributed filter design for time-varying systems under stochastic nonlinearities and sensor degradation. IEEE Trans. Signal Process., 2017, 65: 1300−1309.
  • 50.
    Huang, C.; Ho, D.W.C.; Lu, J.Q. Partial-information-based distributed filtering in two-targets tracking sensor networks. IEEE Trans. Circuits Syst. I Reg. Papers, 2012, 59: 820−832.
  • 51.
    Song, H.Y.; Yu, L.; Zhang, D. Distributed set-valued estimation in sensor networks with limited communication data rate. J. Frankl Inst., 2013, 350: 1264−1283.
  • 52.
    Shen, B.; Wang, Z.D.; Hung, Y.S.; et al. Distributed H∞ filtering for polynomial nonlinear stochastic systems in sensor networks. IEEE Trans. Ind. Electron., 2011, 58: 1971−1979.
  • 53.
    Zhang, W.A.; Dong, H.; Guo, G.; et al. Distributed sampled-data H∞ filtering for sensor networks with nonuniform sampling periods. IEEE Trans. Ind. Inf., 2014, 10: 871−881.
  • 54.
    Su, X.J.; Wu, L.G.; Shi, P. Sensor networks with random link failures: Distributed filtering for T-S fuzzy systems. IEEE Trans. Ind. Inf., 2013, 9: 1739−1750.
  • 55.
    Matei, I.; Baras, J.S. A linear distributed filter inspired by the Markovian jump linear system filtering problem. Automatica, 2012, 48: 1924−1928.
  • 56.
    Matei, I.; Baras, J.S. Consensus-based linear distributed filtering. Automatica, 2012, 48: 1776−1782.
  • 57.
    Chen, Y.G.; Wang, Z.D.; Alsaadi, F.E.; et al. Dynamic output-feedback H∞ control for discrete time-delayed systems with actuator saturations under round-robin communication protocol. Int. J. Robust Nonlinear Control, 2022, 32: 1703−1720.
  • 58.
    Li, X.F.; Fang, J.A.; Li, H.Y. Finite-time synchronization of memristive neural networks with time-varying delays via two control methods. Math. Meth. Appl. Sci., 2019, 42: 2746−2760.
  • 59.
    Liu, A.D.; Zhang, W.A.; Chen, B.; et al. Networked filtering with Markov transmission delays and packet disordering. IET Control Theory Appl., 2018, 12: 687−693.
  • 60.
    Wang, Y.Z.; Wang, Z.D.; Zou, L. et al. H∞ proportional-integral state estimation for T-S fuzzy systems over randomly delayed redundant channels with partly known probabilities. IEEE Trans. Cybern., 2022, 52: 9951−9963.
  • 61.
    Qian, W.; Xing, W.W.; Fei, S.M. H∞ state estimation for neural networks with general activation function and mixed time-varying delays. IEEE Trans. Neural Netw. Learn. Syst., 2021, 32: 3909−3918.
  • 62.
    Zhao, Z.Y.; Yi, X.J.; Ma, L.F.; et al. Quantized recursive filtering for networked systems with stochastic transmission delays. ISA Trans., 2022, 127: 99−107.
  • 63.
    Sun, Y.; Mao, J.Y.; Liu, H.J.; et al. Distributed recursive filtering for discrete time-delayed stochastic nonlinear systems based on fuzzy rules. Neurocomputing, 2020, 400: 412−419.
  • 64.
    Zhao, Z.Y.; Wang, Z.D.; Zou, L.; et al. Finite-horizon H∞ state estimation for artificial neural networks with component-based distributed delays and stochastic protocol. Neurocomputing, 2018, 321: 169−177.
  • 65.
    Chen, Y.; Wang, Z.D.; Yuan, Y.; Date, P. Distributed H∞ filtering for switched stochastic delayed systems over sensor networks with fading measurements. IEEE Trans. Cybern., 2020, 50: 2−14.
  • 66.
    Cloosterman, M.B.G.; Hetel, L.; Van De Wouw, N.; et al. Controller synthesis for networked control systems. Automatica, 2010, 46: 1584−1594.
  • 67.
    Li, J.N.; Er, M.J.; Yu, H.B. Sampling and control strategy: Networked control systems subject to packet disordering. IET Control Theory Appl., 2016, 10: 674−683.
  • 68.
    Liu, A.D.; Zhang, W.A.; Yu, L.; et al. New results on stabilization of networked control systems with packet disordering. Automatica, 2015, 52: 255−259.
  • 69.
    Zhang, X.M.; Han, Q.L. Network-based H∞ filtering for discrete-time systems. IEEE Trans. Signal Process., 2012, 60: 956−961.
  • 70.
    Wang, D.; Wang, Z.D.; Li, G.Y.; et al. Distributed filtering for switched nonlinear positive systems with missing measurements over sensor networks. IEEE Sens. J., 2016, 16: 4940−4948.
  • 71.
    Li, B.; Wang, Z.D.; Han, Q.L.; et al. Distributed quasiconsensus control for stochastic multiagent systems under round-robin protocol and uniform quantization. IEEE Trans. Cybern., 2022, 52: 6721−6732.
  • 72.
    Zou, L.; Wang, Z.D.; Han, Q.L.; et al. Ultimate boundedness control for networked systems with try-once-discard protocol and uniform quantization effects. IEEE Trans. Autom. Control, 2017, 62: 6582−6588.
  • 73.
    Yuan, H.H.; Guo, Y.Z.; Xia, Y.Q. Event-based distributed filtering against deception attacks for sensor networks with quantization effect. ISA Trans., 2022, 126: 338−351.
  • 74.
    Li, T.; Xie, L.H. Distributed coordination of multi-agent systems with quantized-observer based encoding-decoding. IEEE Trans. Autom. Control, 2012, 57: 3023−3037.
  • 75.
    Wang, L.C.; Wang, Z.D.; Han, Q.L.; et al. Synchronization control for a class of discrete-time dynamical networks with packet dropouts: A coding-decoding-based approach. IEEE Trans. Cybern., 2018, 48: 2437−2448.
  • 76.
    Wang, L.C.; Wang, Z.D.; Han, Q.L.; et al. Event-based variance-constrained H∞ filtering for stochastic parameter systems over sensor networks with successive missing measurements. IEEE Trans. Cybern., 2018, 48: 1007−1017.
  • 77.
    Shen, D.; Zhang, C. Zero-error tracking control under unified quantized iterative learning framework via encoding-decoding method. IEEE Trans. Cybern., 2022, 52: 1979−1991.
  • 78.
    Liu, L.; Ma, L.F.; Guo, J.; et al. Distributed set-membership filtering for time-varying systems: A coding-decoding-based approach. Automatica, 2021, 129: 109684.
  • 79.
    Wang, L.C.; Wang, Z.D.; Zhao, D.; et al. Event-based state estimation under constrained bit rate: An encoding-decoding approach. Automatica, 2022, 143: 110421.
  • 80.
    Suo, J.H.; Li, N. Observer-based synchronisation control for discrete-time delayed switched complex networks with coding-decoding approach. Int. J. Syst. Sci., 2022, 53: 2711−2728.
  • 81.
    Jiang, B.; Dong, H.L.; Shen, Y.X.; et al. Encoding-decoding-based recursive filtering for fractional-order systems. IEEE/CAA J. Autom. Sin., 2022, 9: 1103−1106.
  • 82.
    Movaghati, S.; Ardakani, M. Optimum bit-sensor assignment for distributed estimation in inhomogeneous sensor networks. IEEE Commun. Lett., 2014, 18: 668−671.
  • 83.
    Gao, Y.T.; Ma, L.F.; Zhang, M.J.; et al. Distributed set-membership filtering for nonlinear time-varying systems with dynamic coding-decoding communication protocol. IEEE Syst. J., 2022, 16: 2958−2967.
  • 84.
    Wen, P.Y.; Li, X.R.; Hou, N.; et al. Distributed recursive fault estimation with binary encoding schemes over sensor networks. Syst. Sci. Control Eng., 2022, 10: 417−427.
  • 85.
    Zou, L.; Wang, Z.D.; Hu, J.; et al. Communication-protocol-based analysis and synthesis of networked systems: Progress, prospects and challenges. Int. J. Syst. Sci., 2021, 52: 3013−3034.
  • 86.
    Walsh, G.C.; Ye, H.; Bushnell, L.G. Stability analysis of networked control systems. IEEE Trans. Control Syst. Technol., 2002, 10: 438−446.
  • 87.
    Zhu, K.Q.; Hu, J.; Liu, Y.R.; et al. On l2-l∞ output-feedback control scheduled by stochastic communication protocol for two-dimensional switched systems. Int. J. Syst. Sci., 2021, 52: 2961−2976.
  • 88.
    Zou, L.; Wang, Z.D.; Han, Q.L.; et al. Recursive filtering for time-varying systems with random access protocol. IEEE Trans. Autom. Control, 2019, 64: 720−727.
  • 89.
    Qu, F.R.; Zhao, X.; Wang, X.M.; et al. Probabilistic-constrained distributed fusion filtering for a class of time-varying systems over sensor networks: A torus-event-triggering mechanism. Int. J. Syst. Sci., 2022, 53: 1288−1297.
  • 90.
    An, W.J.; Zhao, P.F.; Liu, H.J.; et al. Distributed multi-step subgradient projection algorithm with adaptive event-triggering protocols: A framework of multiagent systems. Int. J. Syst. Sci., 2022, 53: 2758−2772.
  • 91.
    Meng, M.Y.; Chen, T.W. Event based agreement protocols for multi-agent networks. Automatica, 2013, 49: 2125−2132.
  • 92.
    Suo, J.H.; Li, N.; Li, Q. Event-triggered H∞ state estimation for discrete-time delayed switched stochastic neural networks with persistent dwell-time switching regularities and sensor saturations. Neurocomputing, 2021, 455: 297−307.
  • 93.
    Li, N.; Li, Q.; Suo, J.H. Dynamic event-triggered H∞ state estimation for delayed complex networks with randomly occurring nonlinearities. Neurocomputing, 2021, 421: 97−104.
  • 94.
    Shen, Y.X.; Wang, Z.D.; Dong, H.L.; et al. Dynamic event-based recursive filtering for multirate systems with integral measurements over sensor networks. Int. J. Robust Nonlinear Control, 2022, 32: 1374−1392.
  • 95.
    Liu, J.L.; Gu, Y.Y.; Cao, J.; et al. Distributed event-triggered H∞ filtering over sensor networks with sensor saturations and cyber-attacks. ISA Trans., 2018, 81: 63−75.
  • 96.
    Tan, Y.S.; Xiong, M.H.; Niu, B.; et al. Distributed hybrid-triggered H∞ filter design for sensor networked systems with output saturations. Neurocomputing, 2018, 315: 261−271.
  • 97.
    Wan, X.B.; Wang, Z.D.; Wu, M.; et al. H∞ state estimation for discrete-time nonlinear singularly perturbed complex networks under the round-robin protocol. IEEE Trans. Neural Netw. Learn. Syst., 2019, 30: 415−426.
  • 98.
    Yin, X.Y.; Li, Z.J.; Zhang, L.X. et al. Distributed state estimation of sensor-network systems subject to Markovian channel switching with application to a chemical process. IEEE Trans. Syst. Man Cybern. Syst., 2018, 48: 864−874.
  • 99.
    Hu, J.; Wang, Z.D.; Liang, J.L.; et al. Event-triggered distributed state estimation with randomly occurring uncertainties and nonlinearities over sensor networks: A delay-fractioning approach. J. Franklin Inst., 2015, 352: 3750−3763.
  • 100.
    Zhang, D.; Yu, L.; Zhang, W.A. Energy efficient distributed filtering for a class of nonlinear systems in sensor networks. IEEE Sens. J., 2015, 15: 3026−3036.
  • 101.
    Chen, B.S.; Zhang, W.H. Stochastic H2/H∞ control with state-dependent noise. IEEE Trans. Autom. Control, 2004, 49: 45−57.
  • 102.
    Bernstein, D.S.; Haddad, W.M. LQG control with an H∞ performance bound: A Riccati equation approach. IEEE Trans. Autom. Control, 1989, 34: 293−305.
  • 103.
    Zhang, W.H.; Huang, Y.L.; Zhang, H.S. Stochastic H2/H∞ control for discrete-time systems with state and disturbance dependent noise. Automatica, 2007, 43: 513−521.
  • 104.
    Shi, Y.B.; Wang, J.H.; Fang, X.K.; et al. Robust mixed H2/H∞ control for an uncertain wireless sensor network systems with time delay and packet loss. Int. J. Control Autom. Syst., 2021, 19: 88−100.
  • 105.
    Liu, L.; Zhou, W.J.; Fei, M.R.; et al. Distributed fusion estimation for stochastic uncertain systems with network-induced complexity and multiple noise. IEEE Trans. Cybern., 2022, 52: 8753−8765.
  • 106.
    Chen, B.; Hu, G.Q.; Zhang, W.A.; et al. Distributed mixed H2/H∞ fusion estimation with limited communication capacity. IEEE Trans. Autom. Control, 2016, 61: 805−810.
  • 107.
    Chen, C.Y.; Dong, W.J.; Djapic, V. Distributed H2/H∞ filtering over infinite horizon. Int. J. Adapt. Control Signal Process., 2018, 32: 330−343.
  • 108.
    Liu, H.J.; Wang, Z.D.; Fei, W.Y.; et al. H∞ and l2/l∞ state estimation for delayed memristive neural networks on finite horizon: The round-robin protocol. Neural Netw., 2020, 132: 121−130.
  • 109.
    Zhao, D.; Wang, Z.D.; Wei, G.L.; et al. l2/l∞ proportional-integral observer design for systems with mixed time-delays under round-robin protocol. Int. J. Robust Nonlinear Control, 2021, 31: 887−906.
  • 110.
    Zou, L.; Wang, Z.D.; Dong, H.L.; et al. Energy-to-peak state estimation with intermittent measurement outliers: The single-output case. IEEE Trans. Cybern., 2022, 52: 11504−11515.
  • 111.
    Rotea, M.A. The generalized H2 control problem. Automatica, 1993, 29: 373−385.
  • 112.
    Chen, Y.; Chen, C.; Xue, A.K. Distributed non-fragile l2-l∞ filtering over sensor networks with random gain variations and fading measurements. Neurocomputing, 2019, 338: 154−162.
  • 113.
    Shen, H.; Xing, M.P.; Wu, Z.G.; et al. l2/l∞ state estimation for persistent dwell-time switched coupled networks subject to round-robin protocol. IEEE Trans. Neural Netw. Learn. Syst., 2021, 32: 2002−2014.
  • 114.
    Kalman, R.E. A new approach to linear filtering and prediction problems. J. Basic Eng., 1960, 82: 35−45.
  • 115.
    Olfati-Saber, R. Distributed Kalman filtering for sensor networks. In Proceeding of the 46th IEEE Conference on Decision and Control, New Orleans, USA, 12–14 December 2007; IEEE: New Orleans, LAUSA, 2007; pp. 5492–5498. doi: 10.1109/CDC.2007.4434303
  • 116.
    Olfati-Saber, R.; Shamma, J.S. Consensus filters for sensor networks and distributed sensor fusion. In Proceeding of the 44th IEEE Conference on Decision and Control, Seville, Spain, 15 December 2005; IEEE: Seville, Spain, 2005; pp. 6698–6703. doi: 10.1109/CDC.2005.1583238
  • 117.
    Olfati-Saber, R. Kalman-consensus filter: Optimality, stability, and performance. In Proceeding of the 48th IEEE Conference on Decision and Control (CDC) Held Jointly with 2009 28th Chinese Control Conference, Shanghai, China, 15–18 December 2009; IEEE: Shanghai, China, 2009; pp. 7036–7042. doi: 10.1109/CDC.2009.5399678
  • 118.
    Xie, L.H.; Soh, Y.C.; De Souza, C.E. Robust Kalman filtering for uncertain discrete-time systems. IEEE Trans. Autom. Control, 1994, 39: 1310−1314.
  • 119.
    Fu, M.Y.; De Souza, C.E.; Luo, Z.Q. Finite-horizon robust Kalman filter design. IEEE Trans. Signal Process., 2001, 49: 2103−2112.
  • 120.
    Song, W.H.; Wang, J.N.; Wang, C.Y.; et al. A variance-constrained approach to event-triggered distributed extended Kalman filtering with multiple fading measurements. Int. J. Robust Nonlinear Control, 2019, 29: 1558−1576.
  • 121.
    Su, H.S.; Li, Z.H.; Ye, Y.Y. Event-triggered Kalman-consensus filter for two-target tracking sensor networks. ISA Trans., 2017, 71: 103−111.
  • 122.
    Li, Q.; Shen, B.; Wang, Z.D.; et al. Recursive distributed filtering over sensor networks on Gilbert-Elliott channels: A dynamic event-triggered approach. Automatica, 2020, 113: 108681.
  • 123.
    Han, F.; Wang, Z.D.; Dong, H.L.; et al. A local approach to distributed H∞-consensus state estimation over sensor networks under hybrid attacks: Dynamic event-triggered scheme. IEEE Trans. Signal Inf. Proc. Netw., 2022, 8: 556−570.
  • 124.
    Dong, H.L.; Bu, X.Y.; Hou, N.; et al. Event-triggered distributed state estimation for a class of time-varying systems over sensor networks with redundant channels. Inf. Fusion, 2017, 36: 243−250.
  • 125.
    Wen, C.B.; Wang, Z.D.; Geng, T.; et al. Event-based distributed recursive filtering for state-saturated systems with redundant channels. Inf. Fusion, 2018, 39: 96−107.
  • 126.
    Bu, X.Y.; Dong, H.L.; Han, F.; et al. Distributed filtering for time-varying systems over sensor networks with randomly switching topologies under the round-robin protocol. Neurocomputing, 2019, 346: 58−64.
  • 127.
    Sheng, L.; Niu, Y.C.; Gao, M. Distributed resilient filtering for time-varying systems over sensor networks subject to round-robin/stochastic protocol. ISA Trans., 2019, 87: 55−67.
  • 128.
    Han, F.; Wang, Z.D.; Chen, G.R.; et al. Scalable consensus filtering for uncertain systems over sensor networks with round-robin protocol. Int. J. Robust Nonlinear Control, 2021, 31: 1051−1066.
  • 129.
    Chen, S.; Ma, L.F.; Ma, Y.Q. Distributed set-membership filtering for nonlinear systems subject to round-robin protocol and stochastic communication protocol over sensor networks. Neurocomputing, 2020, 385: 13−21.
  • 130.
    Wei, G.L.; Liu, S.; Wang, L.C.; et al. Event-based distributed set-membership filtering for a class of time-varying non-linear systems over sensor networks with saturation effects. Int. J. Gen. Syst., 2016, 45: 532−547.
  • 131.
    Zhao, Z.Y.; Wang, Z.D.; Zou, L.; et al. Event-triggered set-membership state estimation for complex networks: A zonotopes-based method. IEEE Trans. Netw. Sci. Eng., 2022, 9: 1175−1186.
  • 132.
    Pak, J.M.; Ahn, C.K.; Shi, P.; et al. Distributed hybrid particle/FIR filtering for mitigating NLOS effects in TOA-based localization using wireless sensor networks. IEEE Trans. Ind. Electron., 2017, 64: 5182−5191.
  • 133.
    Song, W.H.; Wang, Z.D.; Wang, J.N.; et al. Distributed auxiliary particle filtering with diffusion strategy for target tracking: A dynamic event-triggered approach. IEEE Trans. Signal Process., 2021, 69: 328−340.
  • 134.
    Gu, D.B.; Sun, J.X.; Hu, Z.; et al. Consensus based distributed particle filter in sensor networks. In Proceedings of IEEE International Conference on Information and Automation, Changsha, China, 20–23 June 2008; IEEE: Changsha, China, 2008; pp. 302–307. doi: 10.1109/ICINFA.2008.4608015
  • 135.
    Mohammadi, A.; Asif, A. Distributed particle filter implementation with intermittent/irregular consensus convergence. IEEE Trans. Signal Process., 2013, 61: 2572−2587.
  • 136.
    Yoo, J.; Kim, W.; Kim, H.J. Distributed estimation using online semi-supervised particle filter for mobile sensor networks. IET Control Theory Appl., 2015, 9: 418−427.
  • 137.
    Xia, N.; Yang, F.W.; Han, Q.L. Distributed event-triggered networked set-membership filtering with partial information transmission. IET Control Theory Appl., 2017, 11: 155−163.
  • 138.
    Yang, F.W.; Xia, N.; Han, Q.L. Event-based networked islanding detection for distributed solar PV generation systems. IEEE Trans. Ind. Inform., 2017, 13: 322−329.
  • 139.
    Liu, S.C.; Liu, P.X. Distributed model-based control and scheduling for load frequency regulation of smart grids over limited bandwidth networks. IEEE Trans. Ind. Inform., 2018, 14: 1814−1823.
Share this article:
How to Cite
Wang, Y.-A.; Shen, B.; Zou, L.; Han, Q.-L. A Survey on Recent Advances in Distributed Filtering over Sensor Networks Subject to Communication Constraints. International Journal of Network Dynamics and Intelligence 2023, 2 (2), 100007. https://doi.org/10.53941/ijndi0201007.
RIS
BibTex
Copyright & License
article copyright Image
Copyright (c) 2023 by the authors.