Optimal State Estimation

By D. O. Anderson, J. Moore. 2006

691 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/30956fbe74c741462131c11f2d80512c52f8827c

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Citations in the corpus

A Tractable Framework for Zero-Lower-Bound Gaussian Term Structure Models (2013, unknown): “1 IEKF and CAB-GATSM estimation overview I adopt the IEKF algorithm and notation from Simon (2006). Hence, in what follows, x t and P t are the prior estimates of the state vector and its covariance at the integer time step t, x+t;i and P + t;i are the posterior estimates at iteration i and t, and x”
RSSI-Based Indoor Localization and Tracking Using Sigma-Point Kalman Smoothers (2009, methodology): “Estimates of the forward and backward filter are then optimally combined [29], [52] to generate smoothed estimates in the standard manner.”
Cooperative Filters and Control for Cooperative Exploration (2010, methodology): “The equations for Kalman filters are obtained by canonical procedures, the formulas are derived following textbooks [43]–[45].”
Biogeography-Based Optimization (2008, methodology): “Note that the minimum cost sensor set and its cost will be computer-dependent because of numerical issues in Matlab’s DARE computation.”
Kalman filtering with state constraints: a survey of linear and nonlinear algorithms (2010, methodology): “These modifications include the extended Kalman filter [2], the unscented Kalman filter [3], and the particle filter [4].”
Visual odometry based on stereo image sequences with RANSAC-based outlier rejection scheme (2010, methodology): “A better choice in such cases is the usage of Kalman Filters based on the Unscented Transform (UT) [22].”
Robust Kalman Filter Based on a Generalized Maximum-Likelihood-Type Estimator (2010, methodology): “In contrast to methods dealing with arbitrarily large outliers, such as those discussed above, the -ﬁlter, an approach stemming from robust control [32]–[39] minimizes the worst-case estimation error averaged over all samples by treating modeling errors and uncertainties as unknown-but-bounded…”
Parameter Estimation and Model Selection in Computational Biology (2010, methodology): “Given that, the current a posteriori state and error covariance estimates, denoted ^ xx z k and P z k respectively, are formed using the following equations: We refer to [29] for a rigorous derivation of the equations we presented so far.”
Power System Dynamic State Estimation: Motivations, Definitions, Methodologies, and Future Work (2019, methodology): “It typically consists of two-steps [38], namely a prediction step using (5), and a filtering/update step”