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The Rauch-Tung-Striebel (RTS) and the Mayne-Fraser (MF) algorithms are two of the most popular smoothing schemes to reconstruct the state of a dynamic linear system from measurements collected on a fixed interval.
A new approach to linear filtering and prediction problems
R. E. Kalman · 1960
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New results in linear filtering and prediction theory
R. E. Kalman and R. S. Bucy · 1961
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Maximum likelihood estimates of linear dynamic systems
H. E. Rauch, F. Tung, and C. T. Striebel · 1965
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A solution of the smoothing problem for linear dynamic systems
D.Q. Mayne · 1966
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The optimum linear smoother as a combination of two optimum linear filters
D.C. Fraser and J.E. Potter · 1969
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A unified approach to smoothing formulas
L. Ljung and T. Kailath · 1976
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Robust Bayesian estimation for the linear model and robustifying the Kalman filter
C.J. Masreliez and R.D. Martin · 1977
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Optimal Filtering
B. D. O. Anderson and J. B. Moore · 1979
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On the fixed-interval smoothing problem
J.E. Wall, A.S. Willsky, and N.R. Sandell · 1981
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A geometrical derivation of the fixed interval smoothing algorithm
C.F. Ansley and R. Kohn · 1982
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A new computationally efficient fixed-interval, discrete-time smoother
G.J. Bierman · 1983
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Robust techniques for signal processing: A survey
S.A. Kassam and H.V. Poor · 1985
Cited alongside, same era.
Robust adaptive Kalman filtering for systems with unknown step inputs and non-Gaussian measurement errors
R.L. Kirlin and A. Moghaddamjoo · 1986
Cited alongside, same era.
Robustification of Kalman filter models
R.J. Meinhold and N.D. Singpurwalla · 1989
Cited alongside, same era.
Solution of discrete-time optimal control problems on parallel computers
S.J. Wright · 1990
Cited alongside, same era.
On Kalman filtering, posterior mode estimation, and Fisher scoring in dynamic exponential family regression
L. Fahrmeir and H. Kaufmann · 1991
Cited alongside, same era.
Interior point methods for optimal control of discrete-time systems
S.J. Wright · 1993
Cited alongside, same era.
The marginal likelihood for parameters in a discrete Gauss-Markov process
B.M. Bell · 2000
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Estimation with Applications to Tracking and Navigation
Yaakov Bar-Shalom, X. Rong Li, and Thiagalingam Kirubarajan · 2001
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Global Positioning Systems, Inertial Navigation, and Integration
Mohinder S. Grewal, Lawrence R. Weill, and Angus P. Andrews · 2007
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An inequality constrained nonlinear Kalman-Bucy smoother by interior point likelihood maximization
B. M. Bell, J. V. Burke, and G. Pillonetto · 2009
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Kalman Filtering
Charles Chui and Guanrong Chen · 2009
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Robust Methods with Applications to Kalman Smoothing and Bundle Adjustment
A.Y. Aravkin · 2010
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Robust recursive estimation in the presence of heavy-tailed observation noise
I.C. Schick and S.K. Mitter · 1994
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Kalman filter with outliers and missing observations
T. Cipra and R. Romera · 1997
Cited alongside, same era.
Robust estimation with unknown noise statistics
Z.M. Durovic and B.D. Kovachevic · 1999
Cited alongside, same era.
Robust Kalman Filtering for Signals and Systems with Large Uncertainties
Ian R. Petersen and Andrey V. Savkin · 1999
Cited alongside, same era.
A smoothness priors time-varying ar coefficient modeling of nonstationary covariance time series
G. Kitagawa and Will Gersch
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An ℓ 1 \ell_{1} -Laplace robust Kalman smoother
A.Y. Aravkin, B.M. Bell, J.V. Burke, and G. Pillonetto · 2011
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Doubly robust smoothing of dynamical processes via outlier sparsity constraints
S. Farahmand, G.B. Giannakis, and D. Angelosante · 2011
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Robust and trend following kalman smoothers using student’s t
A. Aravkin, J. Burke, and G. Pillonetto · 2012
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Sparse/robust estimation and kalman smoothing with nonsmooth log-concave densities: Modeling,computation, and theory, 2013
Aleksandr Y Aravkin, James V. Burke, and Gianluigi Pillonetto · 2013
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