Parameter estimation in non-linear state-space models by automatic differentiation of non-linear kalman filters

Ajinkya Gorad; Zheng Zhao; Simo Särkkä

doi:10.1109/MLSP49062.2020.9231844

Parameter estimation in non-linear state-space models by automatic differentiation of non-linear kalman filters

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

509 Lataukset (Pure)

Abstrakti

In this article, we propose automatic differentiation based methods for parameter estimation in non-linear state-space models. We use extended Kalman filter and cubature Kalman filters for approximating the negative log-likelihood (i.e., the energy function) of the parameter posterior distribution and compute the gradients and Hessians of this function by using automatic differentiation of the filter recursions. The proposed approach enables computing MAP estimates and forming Laplace approximations for the parameter posterior without a need for implementing complicated derivative recursions or manual computation of Jacobians. The methods are demonstrated in parameter estimation problems on a pendulum model and coordinated turn model.

Alkuperäiskieli	Englanti
Otsikko	Proceedings of the 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing, MLSP 2020
Kustantaja	IEEE
Sivumäärä	6
ISBN (elektroninen)	9781728166629
DOI - pysyväislinkit	https://doi.org/10.1109/MLSP49062.2020.9231844
Tila	Julkaistu - syysk. 2020
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisussa
Tapahtuma	IEEE International Workshop on Machine Learning for Signal Processing - Aalto University, Espoo, Suomi Kesto: 21 syysk. 2020 → 24 syysk. 2020 Konferenssinumero: 30 https://ieeemlsp.cc

Julkaisusarja

Nimi	IEEE International Workshop on Machine Learning for Signal Processing
Kustantaja	IEEE
ISSN (painettu)	2161-0363
ISSN (elektroninen)	2161-0371

Workshop

Workshop	IEEE International Workshop on Machine Learning for Signal Processing
Lyhennettä	MLSP
Maa/Alue	Suomi
Kaupunki	Espoo
Ajanjakso	21/09/2020 → 24/09/2020
www-osoite	https://ieeemlsp.cc

Pääsy asiakirjaan

10.1109/MLSP49062.2020.9231844

ELEC_Gorad_etal_Parameter-Estimation-in-Non-Linear_MLSP2020_acceptedauthormanuscriptAccepted author manuscript, 506 KB

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Link to publication in Scopus

-: Rinnakkaisen ja hajautetun laskennan menetelmiä bayesilaisille graafisille malleille
Särkkä, S., Merkatas, C., Yamin, A., Corenflos, A., Ma, X., Emzir, M., Yaghoobi, F. & Hassan, S. S.
04/09/2019 → 31/12/2022
Projekti: Academy of Finland: Other research funding

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Gorad, A., Zhao, Z., & Särkkä, S. (2020). Parameter estimation in non-linear state-space models by automatic differentiation of non-linear kalman filters. teoksessa Proceedings of the 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing, MLSP 2020 Artikkeli 9231844 (IEEE International Workshop on Machine Learning for Signal Processing). IEEE. https://doi.org/10.1109/MLSP49062.2020.9231844

@inproceedings{5658326288b74130bf3ffa2ae9df237d,

title = "Parameter estimation in non-linear state-space models by automatic differentiation of non-linear kalman filters",

abstract = "In this article, we propose automatic differentiation based methods for parameter estimation in non-linear state-space models. We use extended Kalman filter and cubature Kalman filters for approximating the negative log-likelihood (i.e., the energy function) of the parameter posterior distribution and compute the gradients and Hessians of this function by using automatic differentiation of the filter recursions. The proposed approach enables computing MAP estimates and forming Laplace approximations for the parameter posterior without a need for implementing complicated derivative recursions or manual computation of Jacobians. The methods are demonstrated in parameter estimation problems on a pendulum model and coordinated turn model. ",

keywords = "Automatic differentiation, Cubature Kalman filter, Extended Kalman filter, Non -linear state space model, Parameter estimation",

author = "Ajinkya Gorad and Zheng Zhao and Simo S{\"a}rkk{\"a}",

year = "2020",

month = sep,

doi = "10.1109/MLSP49062.2020.9231844",

language = "English",

series = "IEEE International Workshop on Machine Learning for Signal Processing",

publisher = "IEEE",

booktitle = "Proceedings of the 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing, MLSP 2020",

address = "United States",

note = "IEEE International Workshop on Machine Learning for Signal Processing, MLSP ; Conference date: 21-09-2020 Through 24-09-2020",

url = "https://ieeemlsp.cc",

}

Gorad, A, Zhao, Z & Särkkä, S 2020, Parameter estimation in non-linear state-space models by automatic differentiation of non-linear kalman filters. julkaisussa Proceedings of the 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing, MLSP 2020., 9231844, IEEE International Workshop on Machine Learning for Signal Processing, IEEE, IEEE International Workshop on Machine Learning for Signal Processing, Espoo, Suomi, 21/09/2020. https://doi.org/10.1109/MLSP49062.2020.9231844

Parameter estimation in non-linear state-space models by automatic differentiation of non-linear kalman filters. / Gorad, Ajinkya; Zhao, Zheng; Särkkä, Simo.
Proceedings of the 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing, MLSP 2020. IEEE, 2020. 9231844 (IEEE International Workshop on Machine Learning for Signal Processing).

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

TY - GEN

T1 - Parameter estimation in non-linear state-space models by automatic differentiation of non-linear kalman filters

AU - Gorad, Ajinkya

AU - Zhao, Zheng

AU - Särkkä, Simo

N1 - Conference code: 30

PY - 2020/9

Y1 - 2020/9

N2 - In this article, we propose automatic differentiation based methods for parameter estimation in non-linear state-space models. We use extended Kalman filter and cubature Kalman filters for approximating the negative log-likelihood (i.e., the energy function) of the parameter posterior distribution and compute the gradients and Hessians of this function by using automatic differentiation of the filter recursions. The proposed approach enables computing MAP estimates and forming Laplace approximations for the parameter posterior without a need for implementing complicated derivative recursions or manual computation of Jacobians. The methods are demonstrated in parameter estimation problems on a pendulum model and coordinated turn model.

AB - In this article, we propose automatic differentiation based methods for parameter estimation in non-linear state-space models. We use extended Kalman filter and cubature Kalman filters for approximating the negative log-likelihood (i.e., the energy function) of the parameter posterior distribution and compute the gradients and Hessians of this function by using automatic differentiation of the filter recursions. The proposed approach enables computing MAP estimates and forming Laplace approximations for the parameter posterior without a need for implementing complicated derivative recursions or manual computation of Jacobians. The methods are demonstrated in parameter estimation problems on a pendulum model and coordinated turn model.

KW - Automatic differentiation

KW - Cubature Kalman filter

KW - Extended Kalman filter

KW - Non -linear state space model

KW - Parameter estimation

UR - http://www.scopus.com/inward/record.url?scp=85096479735&partnerID=8YFLogxK

U2 - 10.1109/MLSP49062.2020.9231844

DO - 10.1109/MLSP49062.2020.9231844

M3 - Conference article in proceedings

AN - SCOPUS:85096479735

T3 - IEEE International Workshop on Machine Learning for Signal Processing

BT - Proceedings of the 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing, MLSP 2020

PB - IEEE

T2 - IEEE International Workshop on Machine Learning for Signal Processing

Y2 - 21 September 2020 through 24 September 2020

ER -

Gorad A, Zhao Z, Särkkä S. Parameter estimation in non-linear state-space models by automatic differentiation of non-linear kalman filters. julkaisussa Proceedings of the 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing, MLSP 2020. IEEE. 2020. 9231844. (IEEE International Workshop on Machine Learning for Signal Processing). doi: 10.1109/MLSP49062.2020.9231844

Parameter estimation in non-linear state-space models by automatic differentiation of non-linear kalman filters

Abstrakti

Julkaisusarja

Workshop

Pääsy asiakirjaan

Muut tiedostot ja linkit

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Projektit

-: Rinnakkaisen ja hajautetun laskennan menetelmiä bayesilaisille graafisille malleille

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Science-IT

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