Variable splitting methods for constrained state estimation in partially observed markov processes
(2020) In IEEE Signal Processing Letters 27. p.1305-1309- Abstract
In this letter, we propose a class of efficient, accurate, and general methods for solving state-estimation problems with equality and inequality constraints. The methods are based on recent developments in variable splitting and partially observed Markov processes. We first present the generalized framework based on variable splitting, then develop efficient methods to solve the state-estimation subproblems arising in the framework. The solutions to these subproblems can be made efficient by leveraging the Markovian structure of the model as is classically done in so-called Bayesian filtering and smoothing methods. The numerical experiments demonstrate that our methods outperform conventional optimization methods in computation cost as... (More)
In this letter, we propose a class of efficient, accurate, and general methods for solving state-estimation problems with equality and inequality constraints. The methods are based on recent developments in variable splitting and partially observed Markov processes. We first present the generalized framework based on variable splitting, then develop efficient methods to solve the state-estimation subproblems arising in the framework. The solutions to these subproblems can be made efficient by leveraging the Markovian structure of the model as is classically done in so-called Bayesian filtering and smoothing methods. The numerical experiments demonstrate that our methods outperform conventional optimization methods in computation cost as well as the estimation performance.
(Less)
- author
- Gao, Rui ; Tronarp, Filip LU and Sarkka, Simo
- publishing date
- 2020
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Constrained state estimation, inequality constraint, Kalman filtering and smoothing, variable splitting
- in
- IEEE Signal Processing Letters
- volume
- 27
- article number
- 9143395
- pages
- 5 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- external identifiers
-
- scopus:85090137750
- ISSN
- 1070-9908
- DOI
- 10.1109/LSP.2020.3010159
- language
- English
- LU publication?
- no
- additional info
- Publisher Copyright: © 1994-2012 IEEE.
- id
- bdd69e06-f5fe-41b2-a6b3-735758ec2b2b
- date added to LUP
- 2023-08-23 15:47:38
- date last changed
- 2023-10-26 17:38:41
@article{bdd69e06-f5fe-41b2-a6b3-735758ec2b2b,
abstract = {{<p>In this letter, we propose a class of efficient, accurate, and general methods for solving state-estimation problems with equality and inequality constraints. The methods are based on recent developments in variable splitting and partially observed Markov processes. We first present the generalized framework based on variable splitting, then develop efficient methods to solve the state-estimation subproblems arising in the framework. The solutions to these subproblems can be made efficient by leveraging the Markovian structure of the model as is classically done in so-called Bayesian filtering and smoothing methods. The numerical experiments demonstrate that our methods outperform conventional optimization methods in computation cost as well as the estimation performance.</p>}},
author = {{Gao, Rui and Tronarp, Filip and Sarkka, Simo}},
issn = {{1070-9908}},
keywords = {{Constrained state estimation; inequality constraint; Kalman filtering and smoothing; variable splitting}},
language = {{eng}},
pages = {{1305--1309}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
series = {{IEEE Signal Processing Letters}},
title = {{Variable splitting methods for constrained state estimation in partially observed markov processes}},
url = {{http://dx.doi.org/10.1109/LSP.2020.3010159}},
doi = {{10.1109/LSP.2020.3010159}},
volume = {{27}},
year = {{2020}},
}