access icon free Distributed consensus filtering for jump Markov linear systems

© The Institution of Engineering and Technology
Received 29/08/2012, Accepted 19/02/2013, Revised 16/01/2013, Published

This article studies the problem of distributed filtering for jump Markov linear systems in a not fully connected sensor network. A distributed consensus filter is developed by applying an improved interacting multiple model approach in which the mode-conditioned estimates are derived by the Kalman consensus filter and the mode probabilities are obtained in the sense of linear minimum variance. A numerical example is provided to demonstrate the effectiveness of the proposed algorithm for tracking a manoeuvring target in a sensor work with eight nodes.

Inspec keywords: target tracking; estimation theory; probability; linear systems; Markov processes; Kalman filters

Other keywords: mode probability; mode conditioned estimation; linear minimum variance; Kalman consensus filter; jump Markov linear system; manoeuvring target tracking; distributed consensus filter; model approach

Subjects: Markov processes; Filtering methods in signal processing; Signal processing theory; Markov processes

References

    1. 1)
      • 7. Cortes, J.: ‘Distributed Kriged Kalman filter for spatial estimation’, IEEE Trans. Autom. Control, 2009, 54, (12), pp. 2816–2827 (doi: 10.1109/TAC.2009.2034192).
    2. 2)
      • 6. Khan, U.A., Moura, J.M.F.: ‘Distributing the Kalman filter for large-scale systems’, IEEE Trans. Signal Process., 2008, 56, (10), pp. 4919–4935 (doi: 10.1109/TSP.2008.927480).
    3. 3)
      • 2. Olfati-Saber, R.: ‘Distributed Kalman filter with embedded consensus filters’. Proc. 44th IEEE Conf. Decision and Control and the European Control Conf., Seville, Spain, December 2005, pp. 8179–8184.
    4. 4)
      • 15. Wang, Z., Niu, Y.: ‘Distributed estimation and filtering for sensor networks’, Int. J. Syst. Sci., 2011, 42, (9), 1421–1425 (doi: 10.1080/00207721.2011.596322).
    5. 5)
      • 1. Olfati-Saber, R., Shamma, J.: ‘Consensus filters for sensor networks and distributed sensor fusion’. Proc. 44th IEEE Conf. Decision and Control and the European Control Conf., Seville, Spain, December 2005, pp. 6698–6703.
    6. 6)
      • 20. Li, X., Jilkov, V.: ‘Survey of maneuvering target tracking. Part V: multiple-model methods’, IEEE Trans. Aerosp. Electron. Syst., 2005, 41, (4), 1255–1321 (doi: 10.1109/TAES.2005.1561886).
    7. 7)
      • 11. Wang, J., Yan, Z.: ‘Coding scheme based on boundary function for consensus control of multi-agent system with time-varying topology’, IET Control Theory Appl., 2012, 6, (10), 1527–1535 (doi: 10.1049/iet-cta.2011.0482).
    8. 8)
      • 18. Wang, Z., Gu, D.: ‘Cooperative target tracking control of multiple robots’, IEEE Trans. Ind. Electron., 2012, 59, (8), 3232–3240 (doi: 10.1109/TIE.2011.2146211).
    9. 9)
      • 5. Carli, R., Chiuso, A., Schenato, L., Zampieri, S.: ‘Distributed Kalman filtering based on consensus strategies’, IEEE J. Sel. Areas Commun., 2008, 26, (4), pp. 622–633 (doi: 10.1109/JSAC.2008.080505).
    10. 10)
      • 8. Farina, M., Ferrari-Trecate, G., Scattolini, R.: ‘Distributed moving horizon estimation for linear constrained systems’, IEEE Trans. Autom. Control, 2010, 55, (11), pp. 2462–2475 (doi: 10.1109/TAC.2010.2046058).
    11. 11)
      • 9. Stanković, S.S., Miloš S. Stanković, Dušan M. Stipanović: ‘Consensus based overlapping decentralized estimator’, IEEE Trans. Autom. Control, 2009, 54, (2), pp. 410–415 (doi: 10.1109/TAC.2008.2009583).
    12. 12)
      • 16. Li, W., Jia, Y.: ‘Consensus-based distributed information filter for a class of jump Markov systems’, IET Control Theory Appl., 2011, 5, (10), 1214–1222 (doi: 10.1049/iet-cta.2010.0240).
    13. 13)
      • 21. Fu, X., Jia, Y., Du, J., Yu, F.: ‘New interacting multiple model algorithms for the tracking of the manoeuvring target’, IET Control Theory Appl., 2010, 4, (10), 2184–2194 (doi: 10.1049/iet-cta.2009.0583).
    14. 14)
      • 12. Wen, G., Duan, Z., Li, Z., Chen, G.: ‘Stochastic consensus in directed networks of agents with non-linear dynamics and repairable actuator failures’, IET Control Theory Appl., 2012, 6, (11), 1583–1593 (doi: 10.1049/iet-cta.2011.0156).
    15. 15)
      • 14. Cui, Y., Jia, Y.: ‘L2-L∞ consensus control for high-order multi-agent systems with switching topologies and time-varying delays’, IET Control Theory Appl., 2012, 6, (12), 1933–1940 (doi: 10.1049/iet-cta.2011.0577).
    16. 16)
      • 17. Li, W., Jia, Y.: ‘Consensus-based distributed multiple model UKF for jump Markov nonlinear systems’, IEEE Trans. Autom. Control, 2012, 57, (1), 227–233 (doi: 10.1109/TAC.2011.2161838).
    17. 17)
      • 19. Blom, H., Bar-Shalom, Y.: ‘The interacting multiple model algorithm for systems with Markovian switching coefficients’, IEEE Trans. Autom. Control, 1988, 33, (8), 780–783 (doi: 10.1109/9.1299).
    18. 18)
      • 13. Hu, Y., Li, P., Lam, J.: ‘Consensus of multi-agent systems: a simultaneous stabilisation approach’, IET Control Theory Appl., 2012, 6, (11), 1758–1765 (doi: 10.1049/iet-cta.2011.0324).
    19. 19)
      • 10. Stanković, S.S., Stanković, M.S., Stipanović, D.M.: ‘Consensus based overlapping decentralized estimation with missing observations and communication faults’, Automatica, 2009, 45, (6), pp. 1397–1406 (doi: 10.1016/j.automatica.2009.02.014).
    20. 20)
      • 22. Sun, S.: ‘Multi-sensor information fusion white noise filter weighted by scalars based on Kalman predictor’, Automatica, 2004, 40, (8), 1447–1453 (doi: 10.1016/j.automatica.2004.03.012).
    21. 21)
      • 3. Olfati-Saber, R.: ‘Distributed Kalman filtering for sensor networks’. Proc. 46th IEEE Conf. Decision and Control, New Orleans, USA, December 2007, pp. 5492–5498.
    22. 22)
      • 4. Olfati-Saber, R.: ‘Kalman-Consensus filter: optimality, stability, and performance’. Proc. 48th IEEE Conf. Decision and Control and 28th Chinese Control Conf., Shanghai, China, December 2009, pp. 7036–7042.
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