Cubature Kalman Filters

By Ienkaran Arasaratnam, Simon S. Haykin. IEEE Transactions on Automatic Control, 2009

3,035 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/b4226bae9693793ae9eb41c9886ead455f5f2cd1

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

1. Simo Särkkä
2. S. Andrew Gadsden
3. Thomas R. Hurd
4. Simon S. Haykin
5. Ienkaran Arasaratnam
6. Fredrik K. Gustafsson
7. Yonina C. Eldar
8. Vince D. Calhoun

In Computer Networks and Communications

1. Simo Särkkä
2. S. Andrew Gadsden
3. Yonina C. Eldar
4. Gustaf Hendeby
5. Fredrik K. Gustafsson
6. Ming Xin
7. Bin Jia
8. Yang Cheng

In Control and Systems Engineering

1. Simo Särkkä
2. S. Andrew Gadsden
3. Thomas R. Hurd
4. Simon S. Haykin
5. Fredrik K. Gustafsson
6. Gustaf Hendeby
7. Ienkaran Arasaratnam
8. Ming Xin

Citations in the corpus

• Some Relations Between Extended and Unscented Kalman Filters (2012, methodology): “The cubature transform (CT), that is used in the cubature Kalman filter (CKF [11]), is derived using different principles than the UT.”
• 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 Filtering for Continuous-Discrete Systems: Theory and Simulations (2010, methodology): “is exactly the same as that of the CKF, the equations of which can be found in [1].”
• Cognitive Control (2012, neutral): “…of approximation to the Bayesian filter; this approximation may take the form of an extended Kalman filter [83], unscented Kalman filter [84], or cubature Kalman filter (CKF) [85] for nonlinear but Gaussian environments, or a particle filter [86], [87] for general nonlinear and non-Gaussian cases.”
• Sparse Gauss-Hermite Quadrature Filter with Application to Spacecraft Attitude Estimation (2011, methodology): “It can be viewed as a special case of the UKF [17].”
• Dynamic modeling of neuronal responses in fMRI using cubature Kalman filtering (2011, methodology): “Our procedure 166 applies a forward pass using the CKF that is finessed by a backward pass of the cubature”
• Applied Stochastic Differential Equations (2019, neutral): “This rule is in fact a special case of the preceding rule, but has turned out to be useful in filtering theory (Arasaratnam and Haykin, 2009; Arasaratnam et al., 2010; Särkkä and Solin, 2012; Särkkä, 2013).”
• Bayesian Filtering and Smoothing (2013, methodology): “, 2006), spherical cubature rules (Arasaratnam and Haykin, 2009), or with many other numerical integration schemes.”
• SMOOTH VARIABLE STRUCTURE FILTERING: THEORY AND APPLICATIONS (2011, methodology): “As described in [72], the goal of the CKF algorithm is to recursively compute the probability 3* |% : + = 1( 7 | , | ) given the posterior 3* |% : + = 1( 7 | , | ) at time .”