access icon free Forward–backward particle smoother for non-linear systems with one-step random measurement delay

This study is concerned with the state smoothing problem for a class of non-linear discrete-time stochastic systems with one-step random measurement delay (ORMD). The main contribution is that the forward–backward particle smoothing scheme is successfully extended to the systems with ORMD. First, the particle filter, specially designed to deal with ORMD, is implemented to obtain the filtering distribution and the joint distribution of state history. Then, by marginalising the joint distribution, the one-step fixed-lag smoothing distribution can be obtained. Finally, based on the forward–backward smoothing scheme, the particle approximation of the fixed-interval smoothing distribution can be obtained by re-weighting the particles which have been used in the foregoing one-step fixed-lag smoothing distribution. Simulation results demonstrate the effectiveness of the proposed smoother.

Inspec keywords: nonlinear control systems; approximation theory; smoothing methods; stochastic systems; discrete time systems; delays

Other keywords: nonlinear discrete-time stochastic systems; forward-backward particle smoother; ORMD; joint distribution; particle approximation; one-step random measurement delay; one-step fixed-lag smoothing distribution

Subjects: Interpolation and function approximation (numerical analysis); Distributed parameter control systems; Nonlinear control systems; Time-varying control systems; Discrete control systems

References

    1. 1)
      • 5. Shi, J., Li, Y., Qi, G., et al: ‘Extended target tracking filter with intermittent observations’, IET Signal Process., 2016, 10, (6), pp. 592602.
    2. 2)
      • 21. Hu, J., Wang, Z., Liu, S., et al: ‘A variance-constrained approach to recursive state estimation for time-varying complex networks with missing measurements’, Automatica, 2016, 64, pp. 155162.
    3. 3)
      • 1. Chitchian, M., Simonetto, A., Amesfoort, A.S.V., et al: ‘Distributed computation particle filters on GPU architectures for real-time control applications’, IEEE Trans. Control Syst. Technol., 2013, 21, (6), pp. 22242238.
    4. 4)
      • 11. Arulampalam, M.S., Maskell, S., Gordon, N., et al: ‘A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking’, IEEE Trans. Signal Process., 2002, 50, (2), pp. 174188.
    5. 5)
      • 14. Nadarajah, N., Tharmarasa, R., McDonald, M., et al: ‘IMM forward filtering and backward smoothing for maneuvering target tracking’, IEEE Trans. Aerosp. Electron. Syst., 2012, 48, (3), pp. 26732678.
    6. 6)
      • 7. Hermoso-Carazo, A., Linares-Pérez, J.: ‘Unscented filtering algorithm using two-step randomly delayed observations in nonlinear systems’, Appl. Math. Model., 2009, 33, (9), pp. 37053717.
    7. 7)
      • 16. Godsill, S.J., Doucet, A., West, M.: ‘Monte Carlo smoothing for nonlinear time series’, J. Am. Stat. Assoc., 2004, 99, (465), pp. 156168.
    8. 8)
      • 6. Hermoso-Carazo, A., Linares-Pérez, J.: ‘Extended and unscented filtering algorithms using one-step randomly delayed observations’, Appl. Math. Comput., 2007, 190, (2), pp. 13751393.
    9. 9)
      • 4. Goh, S.T., Abdelkhalik, O., Zekavat, S.A.: ‘A weighted measurement fusion Kalman filter implementation for UAV navigation’, Aerosp. Sci. Technol., 2013, 28, (1), pp. 315323.
    10. 10)
      • 23. Jing, M., Shuli, S.: ‘Centralized fusion estimators for multisensor systems with random sensor delays, multiple packet dropouts and uncertain observations’, IEEE Sens. J., 2013, 13, (4), pp. 12281235.
    11. 11)
      • 15. Särkkä, S.: ‘Unscented Rauch–Tung–Striebel smoother’, IEEE Trans. Autom. Control, 2008, 53, (3), pp. 845849.
    12. 12)
      • 19. Zhang, Y., Huang, Y., Li, N., et al: ‘Particle filter with one-step randomly delayed measurements and unknown latency probability’, Int. J. Syst. Sci., 2016, 47, (1), pp. 209221.
    13. 13)
      • 3. Yin, S., Zhu, X.: ‘Intelligent particle filter and its application to fault detection of nonlinear system’, IEEE Trans. Ind. Electron., 2015, 62, (6), pp. 38523861.
    14. 14)
      • 12. Cappé, O., Godsill, S.J., Moulines, E.: ‘An overview of existing methods and recent advances in sequential Monte Carlo’, Proc. IEEE, 2007, 95, (5), pp. 899924.
    15. 15)
      • 18. Papi, F., Bocquel, M., Podt, M., et al: ‘Fixed-lag smoothing for Bayes optimal knowledge exploitation in target tracking’, IEEE Trans. Signal Process., 2014, 62, (12), pp. 31433152.
    16. 16)
      • 24. Andrieu, C., Doucet, A., Singh, S.S., et al: ‘Particle methods for change detection, system identification, and control’, Proc. IEEE, 2004, 92, (3), pp. 423438.
    17. 17)
      • 13. Liu, J., Han, C., Prahlad, V.: ‘Process noise identification based particle filter: an efficient method to track highly manoeuvring targets’, IET Signal Process., 2011, 5, (6), pp. 538546.
    18. 18)
      • 20. Särkkä, S.: ‘Bayesian filtering and smoothing’ (Cambridge University Press, Cambridge, 2013).
    19. 19)
      • 22. Hu, J., Wang, Z., Shen, B., et al: ‘Quantised recursive filtering for a class of nonlinear systems with multiplicative noises and missing measurements’, Int. J. Control, 2013, 86, (4), pp. 650663.
    20. 20)
      • 9. Yang, Y., Liang, Y., Pan, Q., et al: ‘Gaussian-consensus filter for nonlinear systems with randomly delayed measurements in sensor networks’, Inf. Fusion, 2016, 30, pp. 92102.
    21. 21)
      • 10. Zhang, W., Zuo, J., Guo, Q., et al: ‘Multisensor information fusion scheme for particle filter’, Electron. Lett., 2015, 51, (6), pp. 486488.
    22. 22)
      • 8. Wang, X., Liang, Y., Pan, Q., et al: ‘Gaussian filter for nonlinear systems with one-step randomly delayed measurements’, Automatica, 2013, 49, (4), pp. 976986.
    23. 23)
      • 2. Yokoyama, N.: ‘Parameter estimation of aircraft dynamics via unscented smoother with expectation–maximization algorithm’, J. Guid. Control Dyn., 2011, 34, (2), pp. 426436.
    24. 24)
      • 17. Doucet, A., Godsill, S., Andrieu, C.: ‘On sequential Monte Carlo sampling methods for Bayesian filtering’, Stat. Comput., 2000, 10, pp. 197208.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-spr.2016.0673
Loading

Related content

content/journals/10.1049/iet-spr.2016.0673
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading