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Binomial Gaussian mixture filter

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Binomial Gaussian mixture filter. / Raitoharju, Matti; Ali-Löytty, Simo; Piché, Robert.

In: Eurasip Journal on Advances in Signal Processing, Vol. 2015, No. 1, 36, 02.12.2015.

Research output: Contribution to journalArticleScientificpeer-review

Harvard

Raitoharju, M, Ali-Löytty, S & Piché, R 2015, 'Binomial Gaussian mixture filter', Eurasip Journal on Advances in Signal Processing, vol. 2015, no. 1, 36. https://doi.org/10.1186/s13634-015-0221-2

APA

Raitoharju, M., Ali-Löytty, S., & Piché, R. (2015). Binomial Gaussian mixture filter. Eurasip Journal on Advances in Signal Processing, 2015(1), [36]. https://doi.org/10.1186/s13634-015-0221-2

Vancouver

Raitoharju M, Ali-Löytty S, Piché R. Binomial Gaussian mixture filter. Eurasip Journal on Advances in Signal Processing. 2015 Dec 2;2015(1). 36. https://doi.org/10.1186/s13634-015-0221-2

Author

Raitoharju, Matti ; Ali-Löytty, Simo ; Piché, Robert. / Binomial Gaussian mixture filter. In: Eurasip Journal on Advances in Signal Processing. 2015 ; Vol. 2015, No. 1.

Bibtex - Download

@article{704dad3f58a846c4acaf9a6b997d471b,
title = "Binomial Gaussian mixture filter",
abstract = "In this work, we present a novel method for approximating a normal distribution with a weighted sum of normal distributions. The approximation is used for splitting normally distributed components in a Gaussian mixture filter, such that components have smaller covariances and cause smaller linearization errors when nonlinear measurements are used for the state update. Our splitting method uses weights from the binomial distribution as component weights. The method preserves the mean and covariance of the original normal distribution, and in addition, the resulting probability density and cumulative distribution functions converge to the original normal distribution when the number of components is increased. Furthermore, an algorithm is presented to do the splitting such as to keep the linearization error below a given threshold with a minimum number of components. The accuracy of the estimate provided by the proposed method is evaluated in four simulated single-update cases and one time series tracking case. In these tests, it is found that the proposed method is more accurate than other Gaussian mixture filters found in the literature when the same number of components is used and that the proposed method is faster and more accurate than particle filters.",
keywords = "Estimation, Gaussian mixture filter, Nonlinear filtering",
author = "Matti Raitoharju and Simo Ali-L{\"o}ytty and Robert Pich{\'e}",
note = "ORG=ase,0.75 ORG=mat,0.25",
year = "2015",
month = "12",
day = "2",
doi = "10.1186/s13634-015-0221-2",
language = "English",
volume = "2015",
journal = "Eurasip Journal on Advances in Signal Processing",
issn = "1687-6172",
publisher = "Springer International Publishing AG",
number = "1",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Binomial Gaussian mixture filter

AU - Raitoharju, Matti

AU - Ali-Löytty, Simo

AU - Piché, Robert

N1 - ORG=ase,0.75 ORG=mat,0.25

PY - 2015/12/2

Y1 - 2015/12/2

N2 - In this work, we present a novel method for approximating a normal distribution with a weighted sum of normal distributions. The approximation is used for splitting normally distributed components in a Gaussian mixture filter, such that components have smaller covariances and cause smaller linearization errors when nonlinear measurements are used for the state update. Our splitting method uses weights from the binomial distribution as component weights. The method preserves the mean and covariance of the original normal distribution, and in addition, the resulting probability density and cumulative distribution functions converge to the original normal distribution when the number of components is increased. Furthermore, an algorithm is presented to do the splitting such as to keep the linearization error below a given threshold with a minimum number of components. The accuracy of the estimate provided by the proposed method is evaluated in four simulated single-update cases and one time series tracking case. In these tests, it is found that the proposed method is more accurate than other Gaussian mixture filters found in the literature when the same number of components is used and that the proposed method is faster and more accurate than particle filters.

AB - In this work, we present a novel method for approximating a normal distribution with a weighted sum of normal distributions. The approximation is used for splitting normally distributed components in a Gaussian mixture filter, such that components have smaller covariances and cause smaller linearization errors when nonlinear measurements are used for the state update. Our splitting method uses weights from the binomial distribution as component weights. The method preserves the mean and covariance of the original normal distribution, and in addition, the resulting probability density and cumulative distribution functions converge to the original normal distribution when the number of components is increased. Furthermore, an algorithm is presented to do the splitting such as to keep the linearization error below a given threshold with a minimum number of components. The accuracy of the estimate provided by the proposed method is evaluated in four simulated single-update cases and one time series tracking case. In these tests, it is found that the proposed method is more accurate than other Gaussian mixture filters found in the literature when the same number of components is used and that the proposed method is faster and more accurate than particle filters.

KW - Estimation

KW - Gaussian mixture filter

KW - Nonlinear filtering

UR - http://www.scopus.com/inward/record.url?scp=84934283964&partnerID=8YFLogxK

U2 - 10.1186/s13634-015-0221-2

DO - 10.1186/s13634-015-0221-2

M3 - Article

VL - 2015

JO - Eurasip Journal on Advances in Signal Processing

JF - Eurasip Journal on Advances in Signal Processing

SN - 1687-6172

IS - 1

M1 - 36

ER -