72 citations · 100 across the 5 of their papers we have counts for
7 papers
Posterior linearisation smoothing with robust iterations
Jakob Lindqvist, Simo Särkkä, Ángel F. García-Fernández +2
This paper considers the problem of iterative Bayesian smoothing in nonlinear state-space models with additive noise using Gaussian approximations. Iterative methods are known to i…
Kalman filtering with empirical noise models
Matti Raitoharju, Henri Nurminen, Demet Cilden-Guler +1
Most Kalman filter extensions assume Gaussian noise and when the noise is non-Gaussian, usually other types of filters are used. These filters, such as particle filter variants, ar…
Damped Posterior Linearization Filter
Matti Raitoharju, Lennart Svensson, Ángel F. García-Fernández +1
The iterated posterior linearization filter (IPLF) is an algorithm for Bayesian state estimation that performs the measurement update using iterative statistical regression. The ma…
A statistical model of tristimulus measurements within and between OLED displays
Matti Raitoharju, Samu Kallio, Matti Pellikka
We present an empirical model for noises in color measurements from OLED displays. According to measured data the noise is not isotropic in the XYZ space, instead most of the noise…
Kullback-Leibler Divergence Approach to Partitioned Update Kalman Filter
Matti Raitoharju, Ángel F. García-Fernández, Robert Piché
Kalman filtering is a widely used framework for Bayesian estimation. The partitioned update Kalman filter applies a Kalman filter update in parts so that the most linear parts of m…
On Computational Complexity Reduction Methods for Kalman Filter Extensions
Matti Raitoharju, Robert Piché
The Kalman filter and its extensions are used in a vast number of aerospace and navigation applications for nonlinear state estimation of time series. In the literature, different…