The square-root unscented Kalman filter for state and parameter-estimation

By Rudolph van der Merwe, Eric A. Wan. 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221), 2001

1,225 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Artificial Intelligence, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/ac3ccaf58ba543fd8d7b787cf939e55345b1659f

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Citations in the corpus

SMOOTH VARIABLE STRUCTURE FILTERING: THEORY AND APPLICATIONS (2011, methodology): “The square-root formulation makes use of three powerful linear algebra techniques: QR decomposition, Cholesky factor updating, and efficient least squares [52,53].”
Unscented filtering and nonlinear estimation (2004, background): “Van Der Merwe [46] has developed a square root formulation of the UT which propagates the mean and square root of the covariance matrix, rather than the covariance matrix itself.”
Optimal PMU Placement for Power System Dynamic State Estimation by Using Empirical Observability Gramian (2014, methodology): “For SR-UKF the mean of the system states at time step 0 is set to be the pre-contingency states, which can be quite different from the real system states, thus making the dynamic state estimation very challenging.”
Industrial Applications of the Kalman Filter: A Review (2013, background): “New high-level theoretical and applied research studies are regularly published, such as [69], [71], and [72], and see [11] for survey.”
Incremental smoothing and mapping (2008, neutral): “Instead, the complexity of the estimation problem can be reduced by omitting the trajectory altogether, as for example pursued by Wang et al. [137] with DSLAM, where measurements are transformed into relative constraints between landmarks.”
Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries (2019, methodology): “3% [194] High P S S / xk yk yk T yk ̃ ̃ ̃ ̃ 1.”
RSSI-Based Indoor Localization and Tracking Using Sigma-Point Kalman Smoothers (2009, methodology): “square-root variants [40].”
Sigma-point kalman filters for probabilistic inference in dynamic state-space models (2004, methodology): “Due to this and the need for more numerical stability (especially d uring the state covariance update), we derived numerically efficient square-rootforms of both the UKF and the CDKF [39, 40].”
On Unscented Kalman Filtering for State Estimation of Continuous-Time Nonlinear Systems (2007, methodology): “To enhance the numerical stability of the update equations, square root versions [4], [43] of the UKF update step can also be used.”
Adaptive Three-Step Kalman Filter for Air Data Sensor Fault Detection and Diagnosis (2016, background): “The UKF is a nonlinear filter that can achieve a better level of accuracy than the extended Kalman filter (EKF) [27,28].”
Gaussian Sum Filters for Space Surveillance: Theory and Simulations (2011, methodology): “For optimal efficiency and numerical stability, the use of the discrete-time square-root version of the UKF [15] is proposed that does not directly form any covariance matrix and instead uses the (lower-triangular) Cholesky factor (i.”