Noise variance estimation for Kalman filter

Noise variance estimation for Kalman filter

In this paper, we propose an algorithm that evaluates noise variance with a numerical integration method. For noise variance estimation, we use Krogh method with a variable integration step. In line with common practice, we limit our study to fourth-order method. First, we perform simulation tests f...

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Main Authors: Beniak Ryszard, Gudzenko Oleksandr, Pyka Tomasz
Format: Article
Language:English
Published: EDP Sciences 2017-01-01
Series:E3S Web of Conferences
Online Access:https://doi.org/10.1051/e3sconf/20171901043
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spelling doaj-4d3127e5d5044eba89febeb1173688542021-02-02T08:56:42ZengEDP SciencesE3S Web of Conferences2267-12422017-01-01190104310.1051/e3sconf/20171901043e3sconf_eems2017_01043Noise variance estimation for Kalman filterBeniak RyszardGudzenko OleksandrPyka TomaszIn this paper, we propose an algorithm that evaluates noise variance with a numerical integration method. For noise variance estimation, we use Krogh method with a variable integration step. In line with common practice, we limit our study to fourth-order method. First, we perform simulation tests for randomly generated signals, related to the transition state and steady state. Next, we formulate three methodologies (research hypotheses) of noise variance estimation, and then compare their efficiency.https://doi.org/10.1051/e3sconf/20171901043
collection DOAJ
language English
format Article
sources DOAJ
author Beniak Ryszard
Gudzenko Oleksandr
Pyka Tomasz
spellingShingle Beniak Ryszard
Gudzenko Oleksandr
Pyka Tomasz
Noise variance estimation for Kalman filter
E3S Web of Conferences
author_facet Beniak Ryszard
Gudzenko Oleksandr
Pyka Tomasz
author_sort Beniak Ryszard
title Noise variance estimation for Kalman filter
title_short Noise variance estimation for Kalman filter
title_full Noise variance estimation for Kalman filter
title_fullStr Noise variance estimation for Kalman filter
title_full_unstemmed Noise variance estimation for Kalman filter
title_sort noise variance estimation for kalman filter
publisher EDP Sciences
series E3S Web of Conferences
issn 2267-1242
publishDate 2017-01-01
description In this paper, we propose an algorithm that evaluates noise variance with a numerical integration method. For noise variance estimation, we use Krogh method with a variable integration step. In line with common practice, we limit our study to fourth-order method. First, we perform simulation tests for randomly generated signals, related to the transition state and steady state. Next, we formulate three methodologies (research hypotheses) of noise variance estimation, and then compare their efficiency.
url https://doi.org/10.1051/e3sconf/20171901043
work_keys_str_mv AT beniakryszard noisevarianceestimationforkalmanfilter
AT gudzenkooleksandr noisevarianceestimationforkalmanfilter
AT pykatomasz noisevarianceestimationforkalmanfilter
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