Recursive Noise Adaptive Kalman Filtering by Variational Bayesian Approximations

By Simo Särkkä, Aapo Nummenmaa. IEEE Transactions on Automatic Control, 2009

644 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/be350fe4ec79800eaac5f79be49d4eacdcbc2a34

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. Vince D. Calhoun
2. Martin Havlicek
3. Milan Brazdil
4. Jirí Jan
5. Jonathon A. Chambers
6. Karl J. Friston
7. Simo Särkkä
8. Rafal Bogacz

In Control and Systems Engineering

1. Vince D. Calhoun
2. Karl J. Friston
3. Simo Särkkä
4. Dominique Chapelle
5. Philippe Moireau

Citations in the corpus

• Bayesian Filtering and Smoothing (2013, methodology): “Variational Bayesian approximations can also be used for estimation of unknown time-varying parameters in state space models (Särkkä and Nummenmaa, 2009).”
• A Novel Adaptive Kalman Filter With Inaccurate Process and Measurement Noise Covariance Matrices (2018, methodology): “The existing variational Bayesian (VB) based AKF (VBAKF) is also an approximation of the Bayesian method, which can estimate an inaccurate and slowly varying measurement noise covariance matrix (MNCM) by choosing appropriate conjugate prior distribution [9]–[12].”
• Dynamic modeling of neuronal responses in fMRI using cubature Kalman filtering (2011, methodology): “We have found a robust 1089 solution (Särkkä and Hartikainen, Under revision; Särkkä and Nummenmaa, 2009) that 1090 combines the variational Bayesian method with the nonlinear Kalman filtering algorithm for the 1091 joint estimation of states and time-varying measurement noise covariance in a nonlin”