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

Research output: Contribution to journal › Article › Scientific › peer-review

56 Citations (Scopus)

698 Downloads (Pure)

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.

Original language	English
Pages (from-to)	408-412
Number of pages	5
Journal	IEEE Signal Processing Letters
Volume	25
Issue number	3
DOIs	https://doi.org/10.1109/LSP.2018.2794767
Publication status	Published - 17 Jan 2018
MoE publication type	A1 Journal article-refereed

Keywords

iterative methods
statistical linear regression
non-linear/non-Gaussian systems
State estimation

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Accepted author manuscript, 320 KB

Science-IT
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School of Science
Facility/equipment: Facility

56 Citations
2 Article

Cooperative localization using posterior linearization belief propagation
Garcia-Fernandez, A. F., Svensson, L. & Särkkä, S., 2018, In: IEEE Transactions on Vehicular Technology. 67, 1, p. 832-836
Research output: Contribution to journal › Article › Scientific › peer-review

Open Access
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62 Citations (Scopus)

378 Downloads (Pure)
Iterated posterior linearisation smoother
Garcia Fernandez, A., Svensson, L. & Särkkä, S., 2017, In: IEEE Transactions on Automatic Control. 62, 4, p. 2056-2063 8 p., 7515187.
Research output: Contribution to journal › Article › Scientific › peer-review
52 Citations (Scopus)

Cite this

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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}",

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AU - Tronarp, Filip

AU - Garcia Fernandez, Angel

AU - Särkkä, Simo

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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

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DO - 10.1109/LSP.2018.2794767

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EP - 412

JO - IEEE Signal Processing Letters

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IS - 3

ER -

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

Abstract

Keywords

Access to Document

Fingerprint

Equipment

Science-IT

Research output

Cooperative localization using posterior linearization belief propagation

Iterated posterior linearisation smoother

Cite this