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

Ajinkya Gorad, Zheng Zhao, Simo Särkkä

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

37 Downloads (Pure)

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.

Original languageEnglish
Title of host publicationProceedings of the 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing, MLSP 2020
PublisherIEEE Computer Society
Number of pages6
ISBN (Electronic)9781728166629
DOIs
Publication statusPublished - Sep 2020
MoE publication typeA4 Article in a conference publication
EventIEEE International Workshop on Machine Learning for Signal Processing - Espoo, Finland
Duration: 21 Sep 202024 Sep 2020
Conference number: 30
https://ieeemlsp.cc

Publication series

NameIEEE International Workshop on Machine Learning for Signal Processing
PublisherIEEE
ISSN (Print)2161-0363
ISSN (Electronic)2161-0371

Workshop

WorkshopIEEE International Workshop on Machine Learning for Signal Processing
Abbreviated titleMLSP
CountryFinland
CityEspoo
Period21/09/202024/09/2020
Internet address

Keywords

  • Automatic differentiation
  • Cubature Kalman filter
  • Extended Kalman filter
  • Non -linear state space model
  • Parameter estimation

Fingerprint Dive into the research topics of 'Parameter estimation in non-linear state-space models by automatic differentiation of non-linear kalman filters'. Together they form a unique fingerprint.

Cite this