An EM Algorithm for Nonlinear State Estimation With Model Uncertainties
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James P. Reilly | Thia Kirubarajan | Kumaradevan Punithakumar | Shahram Shirani | Derek Yee | Amin Zia | T. Kirubarajan | K. Punithakumar | J. Reilly | S. Shirani | D. Yee | A. Zia
[1] Niclas Bergman,et al. Recursive Bayesian Estimation : Navigation and Tracking Applications , 1999 .
[2] D. Magill. Optimal adaptive estimation of sampled stochastic processes , 1965 .
[3] Nando de Freitas,et al. Sequential Monte Carlo Methods in Practice , 2001, Statistics for Engineering and Information Science.
[4] G. McLachlan,et al. The EM algorithm and extensions , 1996 .
[5] Thiagalingam Kirubarajan,et al. Stochastic EM algorithm for nonlinear state estimation with model uncertainties , 2004, SPIE Optics + Photonics.
[6] Kaare Brandt Petersen,et al. The Matrix Cookbook , 2006 .
[7] E. Stear,et al. The simultaneous on-line estimation of parameters and states in linear systems , 1976 .
[8] Simon J. Godsill,et al. On sequential Monte Carlo sampling methods for Bayesian filtering , 2000, Stat. Comput..
[9] P. Mookerjee,et al. Reduced state estimator for systems with parametric inputs , 2004, IEEE Transactions on Aerospace and Electronic Systems.
[10] A. Bagchi,et al. Simultaneous ML estimation of state and parameters for hyperbolic systems with noisy boundary conditions , 1990, 29th IEEE Conference on Decision and Control.
[11] I. Rusnak. Simultaneous identification and tracking of uncertain systems , 1996, Proceedings of 19th Convention of Electrical and Electronics Engineers in Israel.
[12] Branko Ristic,et al. Sensor registration in ECEF coordinates using the MLR algorithm , 2003, Sixth International Conference of Information Fusion, 2003. Proceedings of the.
[13] Demetrios G. Lainiotis,et al. Optimal Estimation in the Presence of Unknown Parameters , 1969, IEEE Trans. Syst. Sci. Cybern..
[14] Jeffrey K. Uhlmann,et al. Unscented filtering and nonlinear estimation , 2004, Proceedings of the IEEE.
[15] Neil J. Gordon,et al. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..
[16] G. Kitagawa. Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models , 1996 .
[17] Amir Averbuch,et al. Interacting Multiple Model Methods in Target Tracking: A Survey , 1988 .
[18] Vladimir Havlena,et al. Simultaneous parameter tracking and state estimation in a linear system , 1993, Autom..
[19] P. Kumar,et al. Theory and practice of recursive identification , 1985, IEEE Transactions on Automatic Control.
[20] Youmin Zhang,et al. Multiple-model estimation with variable structure: likely model set algorithm , 1998, Defense, Security, and Sensing.
[21] Fuqing Zhang,et al. SIMULTANEOUS STATE AND PARAMETER ESTIMATION WITH AN ENSEMBLE KALMAN FILTER FOR THERMALLY DRIVEN CIRCULATIONS . PART I : EXPERIMENTS WITH PERFECT PARAMETERS , 2004 .
[22] Henry Cox,et al. On the estimation of state variables and parameters for noisy dynamic systems , 1964 .
[23] Neil J. Gordon,et al. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..
[24] Thia Kirubarajan,et al. Estimation with Applications to Tracking and Navigation: Theory, Algorithms and Software , 2001 .
[25] Thiagalingam Kirubarajan,et al. Comparison of EKF, pseudomeasurement, and particle filters for a bearing-only target tracking problem , 2002, SPIE Defense + Commercial Sensing.
[26] Jitendra K. Tugnait,et al. Adaptive estimation in linear systems with unknown Markovian noise statistics , 1980, IEEE Trans. Inf. Theory.
[27] Jitendra Tugnait,et al. Adaptive estimation and identification for discrete systems with Markov jump parameters , 1981, 1981 20th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes.
[28] Donald B. Rubin,et al. Max-imum Likelihood from Incomplete Data , 1972 .
[29] Y. Bar-Shalom,et al. Multiple-model estimation with variable structure , 1996, IEEE Trans. Autom. Control..
[30] Youmin Zhang,et al. Multiple-model estimation with variable structure. V. Likely-model set algorithm , 2000, IEEE Trans. Aerosp. Electron. Syst..
[31] Carlos H. Muravchik,et al. Posterior Cramer-Rao bounds for discrete-time nonlinear filtering , 1998, IEEE Trans. Signal Process..
[32] S. Roweis,et al. Learning Nonlinear Dynamical Systems Using the Expectation–Maximization Algorithm , 2001 .
[33] Y. Ho,et al. An approach to the identification and control of linear dynamic systems with unknown parameters , 1963 .