Iterative Filtering and Smoothing In Non-Linear and Non-Gaussian Systems Using Conditional Moments

Filip Tronarp; Angel Garcia Fernandez; Simo Särkkä

doi:10.1109/LSP.2018.2794767

Iterative Filtering and Smoothing In Non-Linear and Non-Gaussian Systems Using Conditional Moments

Filip Tronarp, Angel Garcia Fernandez, Simo Särkkä

University of Liverpool

Tutkimustuotos: Lehtiartikkeli › Article › Scientific › vertaisarvioitu

40 Sitaatiot (Scopus)

563 Lataukset (Pure)

Abstrakti

This letter presents the development of novel iterated filters and smoothers that only require specification of the conditional moments of the dynamic and measurement models. This leads to generalisations of the iterated extended Kalman filter, the iterated extended Kalman smoother, the iterated posterior linearisation filter, and the iterated posterior linearisation smoother. The connections to the previous algorithms are clarified and a convergence analysis is provided. Furthermore, the merits of the proposed algorithms are demonstrated in simulations of the stochastic Ricker map where they are shown to have similar or superior performance to competing algorithms.

Alkuperäiskieli	Englanti
Sivut	408-412
Sivumäärä	5
Julkaisu	IEEE Signal Processing Letters
Vuosikerta	25
Numero	3
DOI - pysyväislinkit	https://doi.org/10.1109/LSP.2018.2794767
Tila	Julkaistu - 17 tammik. 2018
OKM-julkaisutyyppi	A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä

Pääsy asiakirjaan

10.1109/LSP.2018.2794767Lisenssi: Määrittelemätön

cm_parapub
Copyright 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Accepted author manuscript, 320 KB

Science-IT
Mikko Hakala (Manager)
Perustieteiden korkeakoulu
Laitteistot/tilat: Facility

40 Viittaukset
2 Article

Cooperative localization using posterior linearization belief propagation
Garcia-Fernandez, A. F., Svensson, L. & Särkkä, S., 2018, julkaisussa: IEEE Transactions on Vehicular Technology. 67, 1, s. 832-836
Tutkimustuotos: Lehtiartikkeli › Article › Scientific › vertaisarvioitu

Open access
Tiedosto
50 Sitaatiot (Scopus)

344 Lataukset (Pure)
Iterated posterior linearisation smoother
Garcia Fernandez, A., Svensson, L. & Särkkä, S., 2017, julkaisussa: IEEE Transactions on Automatic Control. 62, 4, s. 2056-2063 8 Sivumäärä, 7515187.
Tutkimustuotos: Lehtiartikkeli › Article › Scientific › vertaisarvioitu
35 Sitaatiot (Scopus)

Siteeraa tätä

@article{c4f98ac0686645508b7e41467b12920a,

title = "Iterative Filtering and Smoothing In Non-Linear and Non-Gaussian Systems Using Conditional Moments",

abstract = "This letter presents the development of novel iterated filters and smoothers that only require specification of the conditional moments of the dynamic and measurement models. This leads to generalisations of the iterated extended Kalman filter, the iterated extended Kalman smoother, the iterated posterior linearisation filter, and the iterated posterior linearisation smoother. The connections to the previous algorithms are clarified and a convergence analysis is provided. Furthermore, the merits of the proposed algorithms are demonstrated in simulations of the stochastic Ricker map where they are shown to have similar or superior performance to competing algorithms.",

keywords = "iterative methods, statistical linear regression, non-linear/non-Gaussian systems, State estimation",

author = "Filip Tronarp and {Garcia Fernandez}, Angel and Simo S{\"a}rkk{\"a}",

year = "2018",

month = jan,

day = "17",

doi = "10.1109/LSP.2018.2794767",

language = "English",

volume = "25",

pages = "408--412",

journal = "IEEE Signal Processing Letters",

issn = "1070-9908",

publisher = "IEEE",

number = "3",

}

TY - JOUR

T1 - Iterative Filtering and Smoothing In Non-Linear and Non-Gaussian Systems Using Conditional Moments

AU - Tronarp, Filip

AU - Garcia Fernandez, Angel

AU - Särkkä, Simo

PY - 2018/1/17

Y1 - 2018/1/17

N2 - This letter presents the development of novel iterated filters and smoothers that only require specification of the conditional moments of the dynamic and measurement models. This leads to generalisations of the iterated extended Kalman filter, the iterated extended Kalman smoother, the iterated posterior linearisation filter, and the iterated posterior linearisation smoother. The connections to the previous algorithms are clarified and a convergence analysis is provided. Furthermore, the merits of the proposed algorithms are demonstrated in simulations of the stochastic Ricker map where they are shown to have similar or superior performance to competing algorithms.

AB - This letter presents the development of novel iterated filters and smoothers that only require specification of the conditional moments of the dynamic and measurement models. This leads to generalisations of the iterated extended Kalman filter, the iterated extended Kalman smoother, the iterated posterior linearisation filter, and the iterated posterior linearisation smoother. The connections to the previous algorithms are clarified and a convergence analysis is provided. Furthermore, the merits of the proposed algorithms are demonstrated in simulations of the stochastic Ricker map where they are shown to have similar or superior performance to competing algorithms.

KW - iterative methods

KW - statistical linear regression

KW - non-linear/non-Gaussian systems

KW - State estimation

U2 - 10.1109/LSP.2018.2794767

DO - 10.1109/LSP.2018.2794767

M3 - Article

SN - 1070-9908

VL - 25

SP - 408

EP - 412

JO - IEEE Signal Processing Letters

JF - IEEE Signal Processing Letters

IS - 3

ER -

Iterative Filtering and Smoothing In Non-Linear and Non-Gaussian Systems Using Conditional Moments

Abstrakti

Pääsy asiakirjaan

Sormenjälki

Laitteet

Science-IT

Tutkimustuotos

Cooperative localization using posterior linearization belief propagation

Iterated posterior linearisation smoother

Siteeraa tätä