Risk-sensitive filtering and smoothing for jump Markov non-linear systems based on unscented transform

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Risk-sensitive filtering and smoothing for jump Markov non-linear systems based on unscented transform

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This study is concerned with risk-sensitive filtering and smoothing for a class of discrete-time jump Markov non-linear systems. Using the so-called reference probability method, the authors present a general theoretical framework to yield recursions for deriving filtered and smoothed estimates through identifying the approximations made by the interacting multiple model (IMM) estimation approach. A suboptimal risk-sensitive filtering algorithm is developed by applying the unscented transform (UT) technique and the one-step fixed-lag smoothing result is also presented for such systems. The effectiveness of the proposed algorithms is demonstrated via a manoeuvering target tracking simulation study.

Inspec keywords: nonlinear control systems; discrete time systems; filtering theory; Markov processes; probability

Other keywords: reference probability method; IMM estimation; risk-sensitive filtering; interacting multiple model; jump Markov nonlinear system; risk-sensitive smoothing; unscented transform; discrete-time system

Subjects: Signal processing theory; Other topics in statistics; Discrete control systems; Nonlinear control systems; Markov processes

References

    1. 1)
      • H. Blom , Y. Bar-Shalom . The interacting multiple model algorithm for systems with Markovian switching coefficients. IEEE Trans. Autom. Control , 8 , 780 - 783
    2. 2)
      • M. Jayakumar , R.N. Banavar . Risk-sensitive filters for recursive estimation of motion from images. IEEE Trans. Pattern Anal. Mach. Intell. , 6 , 659 - 666
    3. 3)
      • Speyer, J.L., Fan, C., Banavar, R.N.: `Optimal stochastic estimation with exponential cost criteria', Proc. 31st Conf. on Decision Control, December 1992, Tucson, Arizona, USA, p. 2293–2298.
    4. 4)
      • Orguner, U.: `Improved state estimation for jump Markov linear systems', 2006, PhD, Middle East Technical University, Ankara, Turkey.
    5. 5)
      • S.J. Julier , J.K. Uhlmann , H.F. Durrant-Whyte . A new method for nonlinear transformation of means and covariances in filters and estimators. IEEE Trans. Autom. Control , 3 , 477 - 482
    6. 6)
      • S. Sadhu , S. Bhaumik , A. Doucet , T.K. Ghoshal . Particle method based formulation of risk-sensitive filter. Signal Process. , 3 , 314 - 319
    7. 7)
      • U. Orguner , M. Demirekler . Maximum likelihood estimation of transition probabilities of jump Markov linear systems. IEEE Trans. Signal Process. , 10 , 5093 - 5108
    8. 8)
      • S. Dey , J.B. Moore . Finite-dimensional risk-sensitive filters and smoothers for discrete-time nonlinear systems. IEEE Trans. Autom. Control , 6 , 1234 - 1239
    9. 9)
      • S. Bhaumik , S. Sadhu , T.K. Ghoshal . Risk-sensitive formulation of unscented Kalman filter. IET Control Theory Appl. , 4 , 375 - 382
    10. 10)
      • J.B. Moore , V. Krishnamurthy . De-interleaving pulse trains using discrete-time stochastic dynamic-linear models. IEEE Trans. Signal Process. , 11 , 3092 - 3103
    11. 11)
      • R.E. Helmick , W.D. Blair , S.A. Hoffman . One-step fixed-lag smoothers for Markovian switching systems. IEEE Trans. Autom. Control , 7 , 1051 - 1056
    12. 12)
      • S. Dey , J.B. Moore . Risk-sensitive filtering and smoothing via reference probability methods. IEEE Trans. Autom. Control , 11 , 1587 - 1591
    13. 13)
      • U. Orguner , M. Demirekler . Risk-sensitive filtering for jump Markov linear systems. Automatica , 1 , 109 - 118
    14. 14)
      • R.K. Boel , M.R. James , I.R. Petersen . Robustness and risk-sensitive filtering. IEEE Trans. Autom. Control , 3 , 451 - 461
    15. 15)
      • M. Mahmoud , J. Jiang , Y.M. Zhang . Stochastic stability analysis of fault tolerant control systems in the presence of noise. IEEE Trans. Autom. Control , 11 , 1810 - 1815
    16. 16)
      • N. Yamamoto , L. Bouten . Quantum risk-sensitive estimation and robustness. IEEE Trans. Autom. Control , 1 , 92 - 107
    17. 17)
      • U. Orguner , F. Gustafsson . Risk-sensitive particle filters for mitigating sample impoverishment. IEEE Trans. Signal Process. , 10 , 5001 - 5012
    18. 18)
      • B. Hassibi , A.H. Sayed , T. Kailath . (1999) Indefinite-quadratic estimation and control: a unified approach to .
    19. 19)
      • X.R. Li , P.J. Vesselin . Survey of maneuvering target tracking. Part V: multiple-model methods. IEEE Trans. Aerosp. Electron. Syst. , 4 , 1255 - 1321
    20. 20)
      • R.J. Elliott , L. Aggoun , J.B. Moore . (1994) Hidden Markov models: estimation and control.
    21. 21)
      • R.J. Elliott , F. Dufour , D.D. Sworder . Exact hybrid filters in discrete time. IEEE Trans. Autom. Control , 12 , 1807 - 1810
    22. 22)
      • V.R. Ramezani , S.I. Marcus . Risk-sensitive probability for Markov chains. Syst. Control Lett. , 5 , 493 - 502
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