Iterative Filtering and Smoothing in Nonlinear and Non-Gaussian Systems Using Conditional Moments
(2018) In IEEE Signal Processing Letters 25(1). p.408-412- 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 generalizations of the iterated extended Kalman filter, the iterated extended Kalman smoother, the iterated posterior linearization filter, and the iterated posterior linearization 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 a similar or superior performance to competing algorithms.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/1bb38258-afb4-42ef-86dc-17345b0bb75b
- author
- Tronarp, Filip LU ; Garcia-Fernandez, Angel F and Särkkä, Simo
- publishing date
- 2018
- type
- Contribution to journal
- publication status
- published
- subject
- in
- IEEE Signal Processing Letters
- volume
- 25
- issue
- 1
- pages
- 408 - 412
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- external identifiers
-
- scopus:85040936544
- ISSN
- 1070-9908
- DOI
- 10.1109/LSP.2018.2794767
- language
- English
- LU publication?
- no
- id
- 1bb38258-afb4-42ef-86dc-17345b0bb75b
- date added to LUP
- 2023-08-20 22:32:49
- date last changed
- 2023-11-10 09:53:42
@article{1bb38258-afb4-42ef-86dc-17345b0bb75b, 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 generalizations of the iterated extended Kalman filter, the iterated extended Kalman smoother, the iterated posterior linearization filter, and the iterated posterior linearization 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 a similar or superior performance to competing algorithms.}}, author = {{Tronarp, Filip and Garcia-Fernandez, Angel F and Särkkä, Simo}}, issn = {{1070-9908}}, language = {{eng}}, number = {{1}}, pages = {{408--412}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, series = {{IEEE Signal Processing Letters}}, title = {{Iterative Filtering and Smoothing in Nonlinear and Non-Gaussian Systems Using Conditional Moments}}, url = {{http://dx.doi.org/10.1109/LSP.2018.2794767}}, doi = {{10.1109/LSP.2018.2794767}}, volume = {{25}}, year = {{2018}}, }