Discrete-Time Nonlinear Filtering Algorithms Using Gauss–Hermite Quadrature
By Ienkaran Arasaratnam, Simon S. Haykin, Robert J. R. Elliott. Proceedings of the IEEE, 2007
643 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Oceanography
View Paper: https://www.semanticscholar.org/paper/74fa0e2a27b82930af5c8f18ec3dfa6c42529c71
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- S. Andrew Gadsden
- Simo Särkkä
- Simon S. Haykin
- Ienkaran Arasaratnam
- Yonina C. Eldar
- Yang Cheng
- Ming Xin
- Bin Jia
In Oceanography
- Simo Särkkä
- S. Andrew Gadsden
- Simon S. Haykin
- Ienkaran Arasaratnam
- Yonina C. Eldar
- Ming Xin
- Petar M. Djuric
- Bin Jia
Citations in the corpus
- KalmanNet: Neural Network Aided Kalman Filtering for Partially Known Dynamics (2021, methodology): “More recent non-linear variations are based on numerical integration: UKF [10], the Gauss-Hermite Quadrature [39], and the Cubature KF [40].”
- Cubature Kalman Filters (2009, methodology): “For example, we may assume it to be Gaussian; the nonlinear filters, namely, the extended Kalman filter (EKF) [7], the central-difference Kalman filter (CDKF) [8], [9], the unscented Kalman filter (UKF) [10], and the quadrature Kalman filter (QKF) [11], [12], fall under this first category.”
- Indoor Tracking: Theory, Methods, and Technologies (2015, background): “For more information on these and their variants, see [44]–[46].”
- Bayesian Filtering and Smoothing (2013, methodology): “/ k i th predicted sigma point of the measurement yk at step k Z Normalization constant Zk Normalization constant at the time step k 1 Infinity www.cambridge.org© in this web service Cambridge University Press Cambridge University Press 978-1-107-03065-7 - Bayesian Filtering and Smoothing Simo Särkk”
- Sparse Gauss-Hermite Quadrature Filter with Application to Spacecraft Attitude Estimation (2011, background): “Note that, in the tensor products of X1 X2 and X2 X1, there are two new points added to the point sets along the two axes and one repeated point at the origin contributed from X1, which is considered an old point.”
- SMOOTH VARIABLE STRUCTURE FILTERING: THEORY AND APPLICATIONS (2011, result): “It has been shown to yield more accurate estimation results when compared with the EKF and UKF strategies [72,84].”