Zobrazeno 1 - 10
of 34
pro vyhledávání: '"Raitoharju, Matti"'
Autor:
Lindqvist, Jakob, Särkkä, Simo, García-Fernández, Ángel F., Raitoharju, Matti, Svensson, Lennart
This paper considers the problem of robust iterative Bayesian smoothing in nonlinear state-space models with additive noise using Gaussian approximations. Iterative methods are known to improve smoothed estimates but are not guaranteed to converge, m
Externí odkaz:
http://arxiv.org/abs/2112.03969
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, are computationally more demanding than Kalman type filters. In this pap
Externí odkaz:
http://arxiv.org/abs/2105.08514
The iterated posterior linearization filter (IPLF) is an algorithm for Bayesian state estimation that performs the measurement update using iterative statistical regression. The main result behind IPLF is that the posterior approximation is more accu
Externí odkaz:
http://arxiv.org/abs/1704.01113
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 is along an axis that is parallel to a vector from origin to measured
Externí odkaz:
http://arxiv.org/abs/1608.08596
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 measurements are applied first. In this paper, we generalize partitione
Externí odkaz:
http://arxiv.org/abs/1603.04683
Autor:
Raitoharju, Matti, Piché, Robert
Publikováno v:
in IEEE Aerospace and Electronic Systems Magazine, vol. 34, no. 10, pp. 2-19, 1 Oct. 2019
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 approaches have been proposed to exploit the structure of the state an
Externí odkaz:
http://arxiv.org/abs/1512.03077
In this paper we present a new Kalman filter extension for state update called Partitioned Update Kalman Filter (PUKF). PUKF updates the state using multidimensional measurements in parts. PUKF evaluates the nonlinearity of the measurement function w
Externí odkaz:
http://arxiv.org/abs/1503.02857
Publikováno v:
In Aerospace Science and Technology September 2019 92:66-76
Publikováno v:
In Signal Processing January 2017 130:289-298
Publikováno v:
2022 25th International Conference on Information Fusion (FUSION).
Publisher Copyright: © 2022 International Society of Information Fusion. This paper is concerned with discrete time Kalman-type filtering with state transition and measurement noises that may be non-additive or non-linearly transformed. More specifi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::37750561b5f8efea0321115bc44ce689
https://doi.org/10.23919/fusion49751.2022.9841320
https://doi.org/10.23919/fusion49751.2022.9841320