Introduction to the Kalman filter

By C. Snyder. 2014

1,183 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/12d742f30be03bf76e1e8da5cf1068d752fcba0e

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In Artificial Intelligence

Citations in the corpus

Multiple Futures Prediction (2019, neutral): “The bulk of classical methods focus on using physics based dynamic or kinematic models [38, 21, 25].”
Visual Tracking: an Experimental Survey (2013, positive): “To reduce the search space further motion prediction is done by a linear motion model, which can be described using Kalman filter [93], [94], [95].”
The default mode network in cognition: a topographical perspective (2021, neutral): “In this context, the DMN could be involved in a process that helps distributed signals in peripheral cortical regions to occur closer together in time 105 , a process that is assumed to happen during memory retrieval 106 .”
A Generalized Extended Kalman Filter Implementation for the Robot Operating System (2014, methodology): “The EKF formulation and algorithm are well-known [3, 4, 5].”
Minimal Invasive Equivalent Grid Impedance Estimation in Inductive–Resistive Power Networks Using Extended Kalman Filter (2014, neutral): “Cycle diagram of EKF algorithm for parameter estimation [39].”
Extended Kalman Filtering for Remaining-Useful-Life Estimation of Bearings (2015, methodology): “The Kalman filter (KF) has been used in many applications involving navigation, online system identification, and tracking [48], [49].”
Artifact Removal in Physiological Signals—Practices and Possibilities (2012, neutral): “are not strictly linear, the Kalman filter has also been extended to the nonlinear domain through the extended Kalman filter (EKF) [64].”