Marginalized adaptive particle filtering for nonlinear models with unknown time-varying noise parameters
Result description
Knowledge of noise distribution is typically crucial for good estimation of a non-linear state-space model. However, properties of the noise process are often unknown in the majority of practical applications. Moreover, distribution of the noise may be non-stationary or state dependent, which prevents the use of off-line tuning methods. General estimation methods, such as particle filtering can be used to estimate the noise parameters, however at the price of heavy computational load. In this paper, wepresent an approach based on marginalized particle filtering where the noise parameters have analytical distribution. Explicit modeling of parameter non-stationarity is avoided and it is replaced by maximum-entropy estimation based on the assumption of slowly varying parameters. Properties of the resulting algorithm are illustrated on both a standard example and a navigation application based on odometry. The latter involves formulas for dead reckoning rotational speeds of two wheels wit
Keywords
Unknown Noise StatisticsAdaptive FilteringMarginalized Particle FilterBayesian Conjugate prior
The result's identifiers
Result code in IS VaVaI
Result on the web
DOI - Digital Object Identifier
Alternative languages
Result language
angličtina
Original language name
Marginalized adaptive particle filtering for nonlinear models with unknown time-varying noise parameters
Original language description
Knowledge of noise distribution is typically crucial for good estimation of a non-linear state-space model. However, properties of the noise process are often unknown in the majority of practical applications. Moreover, distribution of the noise may be non-stationary or state dependent, which prevents the use of off-line tuning methods. General estimation methods, such as particle filtering can be used to estimate the noise parameters, however at the price of heavy computational load. In this paper, wepresent an approach based on marginalized particle filtering where the noise parameters have analytical distribution. Explicit modeling of parameter non-stationarity is avoided and it is replaced by maximum-entropy estimation based on the assumption of slowly varying parameters. Properties of the resulting algorithm are illustrated on both a standard example and a navigation application based on odometry. The latter involves formulas for dead reckoning rotational speeds of two wheels wit
Czech name
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Czech description
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Classification
Type
Jx - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
BC - Theory and management systems
OECD FORD branch
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Result continuities
Project
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2013
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Automatica
ISSN
0005-1098
e-ISSN
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Volume of the periodical
49
Issue of the periodical within the volume
6
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
10
Pages from-to
1566-1575
UT code for WoS article
000319540500005
EID of the result in the Scopus database
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Basic information
Result type
Jx - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP
BC - Theory and management systems
Year of implementation
2013