Kalman Filtering: Theory and Practice Using MATLAB

By M. Grewal, A. Andrews. 2001

1,199 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/adb34a1b268f4d5f233c0649222153a53f687aaa

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Citations in the corpus

Li-Ion Battery SOC Estimation Method based on the Reduced Order Extended Kalman Filtering (2006, unknown): “E-mail address: jmoonzz@shinbiro.com (J. Lee). tational complex is (l∗(l2 + n2)) [ 9 ].”
Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries (2019, unknown): “Linear Kalman filter (LKF) is a well-known mathematical estimation method introduced in 1960 [185] that uses discretised mathematical equations of the time domain linear dynamic system.”
Applied Stochastic Differential Equations (2019, unknown): “Stochastic differential equation models of this kind are commonly used in navigation and control systems (see, e.g., Jazwinski, 1970; Bar-Shalom et al., 2001; Grewal and Andrews, 2001).”
A natural observer for optimal state estimation in second order linear structural systems (2011, background): “In that case the Kalman gain [8,14,21] is the optimal choice for the feedback matrix G(t), in the sense that it minimizes the trace of the state error covariance.”
State of Charge Estimation for Lithium-Ion Battery Based on Nonlinear Observer: An H∞ Method (2017, unknown): “The most widely-used filter technique is the various Kalman filters (KF) [5].”
Kalman filtering and smoothing solutions to temporal Gaussian process regression models (2010, background): “This representation is known as the companion form [ 13 ] of (1), and there exists other forms as well (such as the canonical controllable and observable forms [14]), which could be more stable numerically, should some problems arise during the inference.”
Nonlinear Dynamical System Identification from Uncertain and Indirect Measurements (2004, unknown): “…for the optimal functioning of the KF. Tutorials and textbooks about Kalman filtering , including applications, are [Jazwinski, 1970; Gelb, 1974 ; Bar-Shalom & Fortmann, 1988; Aoki, 1990; Hamilton, 1994; Mendel, 1995; Gershenfeld, 1999; Grewal & Andrews, 2001; Honerkamp, 2002; Walker, 2002].”
Lattice Discrete Particle Model (LDPM) for failure behavior of concrete. II: Calibration and validation (2011, background): “[12] M.”
Analysis of Multifactor Affine Yield Curve Models (2008, unknown): “In such cases, we invoke the square root filter (Anderson and Moore 1979; Grewal and Andrews 2001), which tends to be more stable than the Kalman filter because the state covariance matrixes are propagated in square root form.”
Motion artifacts in functional near-infrared spectroscopy: A comparison of motion correction techniques applied to real cognitive data (2014, unknown): “Then, when the measured signal at time = t k + 1 comes, it is used to update and correct the predicted state x k + 1 , which is then used again in the prediction of the next state (for more information on the Kalman filter theory see Grewal and Andrews, 2001; Haykin, 2001).”
SMOOTH VARIABLE STRUCTURE FILTERING: THEORY AND APPLICATIONS (2011, unknown): “1 1.2 Research Contributions and Novelty ............................................................................... 4 1.3 Organization of the Thesis ............................................................................................... 6 2 Literature Review ............................”