Kalman smoother error bounds in the presence of misspecified measurements

Ron Teichner; Ron Meir

doi:https://doi.org/10.1016/j.ifacol.2023.10.907

Kalman smoother error bounds in the presence of misspecified measurements

Ron Teichner, Ron Meir

Technion - Israel Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The performance of a discrete-time fixed-interval Kalman smoother in the presence of a misspecified measurement equation is considered. An easily calculable numerical bound for the increment in smoothing error energy is provided in an adversarial non-linear setting by formulating a high-dimensional optimization problem which is solvable via its Lagrange dual, a scalar convex optimization problem. The results are validated by an adversarial learner, implemented as a neural network.

Original language	English
Title of host publication	IFAC-PapersOnLine
Editors	Hideaki Ishii, Yoshio Ebihara, Jun-ichi Imura, Masaki Yamakita
Publisher	Elsevier B.V.
Pages	10252-10257
Number of pages	6
Edition	2
ISBN (Electronic)	9781713872344
DOIs	https://doi.org/10.1016/j.ifacol.2023.10.907
State	Published - 1 Jul 2023
Event	22nd IFAC World Congress - Yokohama, Japan Duration: 9 Jul 2023 → 14 Jul 2023

Publication series

Name	IFAC-PapersOnLine
Number	2
Volume	56

Conference

Conference	22nd IFAC World Congress
Country/Territory	Japan
City	Yokohama
Period	9/07/23 → 14/07/23

Keywords

adversary
Kalman
smoothing

All Science Journal Classification (ASJC) codes

Control and Systems Engineering

Access to Document

https://doi.org/10.1016/j.ifacol.2023.10.907

Cite this

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title = "Kalman smoother error bounds in the presence of misspecified measurements",

abstract = "The performance of a discrete-time fixed-interval Kalman smoother in the presence of a misspecified measurement equation is considered. An easily calculable numerical bound for the increment in smoothing error energy is provided in an adversarial non-linear setting by formulating a high-dimensional optimization problem which is solvable via its Lagrange dual, a scalar convex optimization problem. The results are validated by an adversarial learner, implemented as a neural network.",

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Kalman smoother error bounds in the presence of misspecified measurements. / Teichner, Ron; Meir, Ron.
IFAC-PapersOnLine. ed. / Hideaki Ishii; Yoshio Ebihara; Jun-ichi Imura; Masaki Yamakita. 2. ed. Elsevier B.V., 2023. p. 10252-10257 (IFAC-PapersOnLine; Vol. 56, No. 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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AU - Meir, Ron

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N2 - The performance of a discrete-time fixed-interval Kalman smoother in the presence of a misspecified measurement equation is considered. An easily calculable numerical bound for the increment in smoothing error energy is provided in an adversarial non-linear setting by formulating a high-dimensional optimization problem which is solvable via its Lagrange dual, a scalar convex optimization problem. The results are validated by an adversarial learner, implemented as a neural network.

AB - The performance of a discrete-time fixed-interval Kalman smoother in the presence of a misspecified measurement equation is considered. An easily calculable numerical bound for the increment in smoothing error energy is provided in an adversarial non-linear setting by formulating a high-dimensional optimization problem which is solvable via its Lagrange dual, a scalar convex optimization problem. The results are validated by an adversarial learner, implemented as a neural network.

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DO - https://doi.org/10.1016/j.ifacol.2023.10.907

M3 - منشور من مؤتمر

T3 - IFAC-PapersOnLine

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EP - 10257

BT - IFAC-PapersOnLine

A2 - Ishii, Hideaki

A2 - Ebihara, Yoshio

A2 - Imura, Jun-ichi

A2 - Yamakita, Masaki

PB - Elsevier B.V.

T2 - 22nd IFAC World Congress

Y2 - 9 July 2023 through 14 July 2023

ER -

Kalman smoother error bounds in the presence of misspecified measurements

Abstract

Publication series

Conference

Keywords

All Science Journal Classification (ASJC) codes

Access to Document

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