Inverse Kalman filtering problems for discrete-time systems
In this paper, several inverse Kalman filtering problems are addressed, where unknown parameters and/or inputs in a filtering model are reconstructed from observations of the posterior estimates that can be noisy or incomplete. In particular, duality in inverse filtering and inverse optimal control...
Saved in:
Main Authors: | , , , |
---|---|
Other Authors: | |
Format: | Article |
Language: | English |
Published: |
2024
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/178696 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
id |
sg-ntu-dr.10356-178696 |
---|---|
record_format |
dspace |
spelling |
sg-ntu-dr.10356-1786962024-07-05T15:39:37Z Inverse Kalman filtering problems for discrete-time systems Li, Yibei Wahlberg, Bo Hu, Xiaoming Xie, Lihua School of Electrical and Electronic Engineering Engineering Inverse filtering Kalman filter In this paper, several inverse Kalman filtering problems are addressed, where unknown parameters and/or inputs in a filtering model are reconstructed from observations of the posterior estimates that can be noisy or incomplete. In particular, duality in inverse filtering and inverse optimal control is studied. It is shown that identifiability and solvability of the inverse Kalman filtering is closely related to that of an inverse linear quadratic regulator (LQR). Covariance matrices of model uncertainties are estimated by solving a well-posed inverse LQR problem. Identifiability of the considered inverse filtering models is established and least squares estimators are designed to be statistically consistent. In addition, algorithms are proposed to reconstruct the unknown sensor parameters as well as raw sensor measurements. Effectiveness and efficiency of the proposed methods are illustrated by numerical simulations. Nanyang Technological University Published version This work was partially supported by the Wallenberg-NTU Postdoctoral Fellowship, the Wallenberg AI, Autonomous Systems and Software Program (WASP), and the Swedish Research Council. 2024-07-02T07:27:35Z 2024-07-02T07:27:35Z 2024 Journal Article Li, Y., Wahlberg, B., Hu, X. & Xie, L. (2024). Inverse Kalman filtering problems for discrete-time systems. Automatica, 163, 111560-. https://dx.doi.org/10.1016/j.automatica.2024.111560 0005-1098 https://hdl.handle.net/10356/178696 10.1016/j.automatica.2024.111560 2-s2.0-85184659997 163 111560 en WASP Automatica © 2024 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). application/pdf |
institution |
Nanyang Technological University |
building |
NTU Library |
continent |
Asia |
country |
Singapore Singapore |
content_provider |
NTU Library |
collection |
DR-NTU |
language |
English |
topic |
Engineering Inverse filtering Kalman filter |
spellingShingle |
Engineering Inverse filtering Kalman filter Li, Yibei Wahlberg, Bo Hu, Xiaoming Xie, Lihua Inverse Kalman filtering problems for discrete-time systems |
description |
In this paper, several inverse Kalman filtering problems are addressed, where unknown parameters and/or inputs in a filtering model are reconstructed from observations of the posterior estimates that can be noisy or incomplete. In particular, duality in inverse filtering and inverse optimal control is studied. It is shown that identifiability and solvability of the inverse Kalman filtering is closely related to that of an inverse linear quadratic regulator (LQR). Covariance matrices of model uncertainties are estimated by solving a well-posed inverse LQR problem. Identifiability of the considered inverse filtering models is established and least squares estimators are designed to be statistically consistent. In addition, algorithms are proposed to reconstruct the unknown sensor parameters as well as raw sensor measurements. Effectiveness and efficiency of the proposed methods are illustrated by numerical simulations. |
author2 |
School of Electrical and Electronic Engineering |
author_facet |
School of Electrical and Electronic Engineering Li, Yibei Wahlberg, Bo Hu, Xiaoming Xie, Lihua |
format |
Article |
author |
Li, Yibei Wahlberg, Bo Hu, Xiaoming Xie, Lihua |
author_sort |
Li, Yibei |
title |
Inverse Kalman filtering problems for discrete-time systems |
title_short |
Inverse Kalman filtering problems for discrete-time systems |
title_full |
Inverse Kalman filtering problems for discrete-time systems |
title_fullStr |
Inverse Kalman filtering problems for discrete-time systems |
title_full_unstemmed |
Inverse Kalman filtering problems for discrete-time systems |
title_sort |
inverse kalman filtering problems for discrete-time systems |
publishDate |
2024 |
url |
https://hdl.handle.net/10356/178696 |
_version_ |
1806059831892639744 |