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Volumn 26, Issue 2, 2011, Pages 194-201

Sequential Monte Carlo filters for abruptly changing state estimation

Author keywords

Abrupt state transition; Bayesian filtering; Degeneracy problem; Maximum entropy particle filter; Sequential importance resampling

Indexed keywords

BAYESIAN FILTERING; DEGENERACY PROBLEM; MAXIMUM ENTROPY PARTICLE FILTER; SEQUENTIAL IMPORTANCE RESAMPLING; STATE TRANSITIONS;

EID: 79952195378     PISSN: 02668920     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.probengmech.2010.07.010     Document Type: Article
Times cited : (10)

References (31)
  • 1
    • 0003665481 scopus 로고    scopus 로고
    • A. Doucet, N. Freitas, N. Gordon, Springer New York
    • A. Doucet, N. Freitas, N. Gordon, Sequential Monte Carlo methods in practice 2001 Springer New York
    • (2001) Sequential Monte Carlo Methods in Practice
  • 3
    • 56449085264 scopus 로고    scopus 로고
    • A Kalman filter based strategy for linear structural system identification based on multiple static and dynamic test data
    • R. Tipireddy, H.A. Nasrellah, and C.S. Manohar A Kalman filter based strategy for linear structural system identification based on multiple static and dynamic test data Probab Eng Mech 24 2009 60 74
    • (2009) Probab Eng Mech , vol.24 , pp. 60-74
    • Tipireddy, R.1    Nasrellah, H.A.2    Manohar, C.S.3
  • 4
    • 0001157746 scopus 로고
    • On the differential equations satisfied by conditional probability densities of Markov processes, with applications
    • H.J. Kushner On the differential equations satisfied by conditional probability densities of Markov processes, with applications SIAM J Control Ser A 2 1962 106 119
    • (1962) SIAM J Control ser A , vol.2 , pp. 106-119
    • Kushner, H.J.1
  • 5
    • 0001355394 scopus 로고
    • Conditional Markov processes
    • R.L. Stratonovich Conditional Markov processes Theory Probab Appl 5 1960 156 178
    • (1960) Theory Probab Appl , vol.5 , pp. 156-178
    • Stratonovich, R.L.1
  • 7
    • 85024423711 scopus 로고
    • New results in linear filtering and prediction theory
    • R.E. Kalman, and R.S. Bucy New results in linear filtering and prediction theory J Basic Eng D 83 1960 95 108
    • (1960) J Basic Eng D , vol.83 , pp. 95-108
    • Kalman, R.E.1    Bucy, R.S.2
  • 8
    • 0028554556 scopus 로고
    • Advanced data assimilation in strongly nonlinear dynamical systems
    • R.N. Miller, M. Ghil, and P. Gauthiez Advanced data assimilation in strongly nonlinear dynamical systems J Atmos Sci 51 1994 1037 1056
    • (1994) J Atmos Sci , vol.51 , pp. 1037-1056
    • Miller, R.N.1    Ghil, M.2    Gauthiez, P.3
  • 9
    • 63449107832 scopus 로고    scopus 로고
    • Monte Carlo-based filtering for fatigue crack growth estimation
    • F. Cadini, E. Zio, and D. Avram Monte Carlo-based filtering for fatigue crack growth estimation Probab Eng Mech 24 2009 367 373
    • (2009) Probab Eng Mech , vol.24 , pp. 367-373
    • Cadini, F.1    Zio, E.2    Avram, D.3
  • 10
    • 0027580559 scopus 로고
    • Novel approach to nonlinear/non-Gaussian Bayesian state estimation
    • N. Gordon, D. Salmond, and A.F.M. Smith Novel approach to nonlinear/non-Gaussian Bayesian state estimation IEE Proc F 140 1993 107 113
    • (1993) IEE Proc F , vol.140 , pp. 107-113
    • Gordon, N.1    Salmond, D.2    Smith, A.F.M.3
  • 11
    • 0001534675 scopus 로고    scopus 로고
    • Nonlinear filtering: Interacting particle solution
    • P.D. Moral Nonlinear filtering: interacting particle solution Markov Process Related Fields 2 1996 555 579
    • (1996) Markov Process Related Fields , vol.2 , pp. 555-579
    • Moral, P.D.1
  • 12
    • 0013326015 scopus 로고    scopus 로고
    • Nonlinear filtering using random particles
    • P.D. Moral Nonlinear filtering using random particles Theory Probab Appl 40 1996 690 701
    • (1996) Theory Probab Appl , vol.40 , pp. 690-701
    • Moral, P.D.1
  • 13
    • 0036475447 scopus 로고    scopus 로고
    • A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking
    • DOI 10.1109/78.978374, PII S1053587X0200569X
    • M.S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking IEEE Trans Signal Process 50 2 2002 174 188 (Pubitemid 34291500)
    • (2002) IEEE Transactions on Signal Processing , vol.50 , Issue.2 , pp. 174-188
    • Arulampalam, M.S.1    Maskell, S.2    Gordon, N.3    Clapp, T.4
  • 14
    • 0000601815 scopus 로고
    • Inverse methods and data assimilation in nonlinear ocean models
    • G. Evensen Inverse methods and data assimilation in nonlinear ocean models Physica D 77 1994 108 129
    • (1994) Physica D , vol.77 , pp. 108-129
    • Evensen, G.1
  • 15
    • 0028193070 scopus 로고
    • Sequential data assimilation with a nonlinear quasigeostrophic model using Monte Carlo methods to forecast error statistics
    • G. Evensen Sequential data assimilation with a nonlinear quasigeostrophic model using Monte Carlo methods to forecast error statistics J Geophys Res 99 C5 1994 10143 10162
    • (1994) J Geophys Res , vol.99 , Issue.C5 , pp. 10143-10162
    • Evensen, G.1
  • 18
    • 0036646756 scopus 로고    scopus 로고
    • Measuring dynamical prediction utility using relative entropy
    • R. Kleeman Measuring dynamical prediction utility using relative entropy J Atmos Sci 59 2002 2057 2072 (Pubitemid 34817275)
    • (2002) Journal of the Atmospheric Sciences , vol.59 , Issue.13 , pp. 2057-2072
    • Kleeman, R.1
  • 20
    • 0034186948 scopus 로고    scopus 로고
    • Gaussian filters for nonlinear filtering problems
    • K. Ito, and K. Xiong Gaussian filters for nonlinear filtering problems IEEE Trans Automat Control 45 2000 910 927
    • (2000) IEEE Trans Automat Control , vol.45 , pp. 910-927
    • Ito, K.1    Xiong, K.2
  • 21
    • 0002348641 scopus 로고
    • Bayesian analysis using Monte Carlo integrationa powerful methodology for handling some difficult problems
    • L. Stewart Bayesian analysis using Monte Carlo integrationa powerful methodology for handling some difficult problems J R Stat Soc Ser D (The Statistician) 32 1983 195 200
    • (1983) J R Stat Soc ser D (The Statistician) , vol.32 , pp. 195-200
    • Stewart, L.1
  • 22
    • 0034382275 scopus 로고    scopus 로고
    • Convergence of empirical processes for interacting particle systems with applications to non-linear filtering
    • P.D. Moral, and M. Ledoux Convergence of empirical processes for interacting particle systems with applications to non-linear filtering J Theoret Probab 13 2000 225 257
    • (2000) J Theoret Probab , vol.13 , pp. 225-257
    • Moral, P.D.1    Ledoux, M.2
  • 23
    • 21344490247 scopus 로고
    • Sequential imputation and Bayesian missing data problem
    • A. Kong, J.S. Liu, and W.H. Wong Sequential imputation and Bayesian missing data problem J Amer Statist Assoc 90 1994 567 576
    • (1994) J Amer Statist Assoc , vol.90 , pp. 567-576
    • Kong, A.1    Liu, J.S.2    Wong, W.H.3
  • 24
    • 1542427941 scopus 로고    scopus 로고
    • Filtering via simulation: Auxiliary particle filters
    • M.K. Pitt, and N. Shephard Filtering via simulation: auxiliary particle filters J Amer Statist Assoc 94 1999 590 599
    • (1999) J Amer Statist Assoc , vol.94 , pp. 590-599
    • Pitt, M.K.1    Shephard, N.2
  • 25
    • 0142010030 scopus 로고    scopus 로고
    • A variance-minimizing filter for large-scale application
    • P.J.V. Leeuwen A variance-minimizing filter for large-scale application Mon Weather Rev 131 2003 2071 2084
    • (2003) Mon Weather Rev , vol.131 , pp. 2071-2084
    • Leeuwen, P.J.V.1
  • 26
    • 0032439201 scopus 로고    scopus 로고
    • Analysis scheme in the ensemble Kalman filter
    • G.P.J. Burgers, Leeuwen van, and G. Evensen Analysis scheme in the ensemble Kalman filter Mon Weather Rev 126 1998 1719 1724 (Pubitemid 128597660)
    • (1998) Monthly Weather Review , vol.126 , Issue.6 , pp. 1719-1724
    • Burgers, G.1    Van Leeuwen, P.J.2    Evensen, G.3
  • 28
    • 25444532546 scopus 로고    scopus 로고
    • A practical computational framework for the multidimensional moment-constrained maximum entropy principle
    • DOI 10.1016/j.jcp.2005.05.008, PII S0021999105002688
    • R. Abramov A practical computational framework for the multidimensional moment-constrained maximum entropy principle J Comput Phys 211 2006 198 209 (Pubitemid 41363981)
    • (2006) Journal of Computational Physics , vol.211 , Issue.1 , pp. 198-209
    • Abramov, R.1
  • 29
    • 8744297806 scopus 로고    scopus 로고
    • A mathematical framework for quantifying predictability through relative entropy
    • A. Majda, R. Kleeman, and D. Cai A mathematical framework for quantifying predictability through relative entropy Methods Appl Anal 9 2002 425 444
    • (2002) Methods Appl Anal , vol.9 , pp. 425-444
    • Majda, A.1    Kleeman, R.2    Cai, D.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.