Minimax Performance Limits for Multiple-Model Estimation
Kjellqvist, Olle LU
(2024)
p.2540-2546
- Abstract
- This article concerns the performance limits of strictly causal state estimation for linear systems with fixed, but uncertain, parameters belonging to a finite set. In particular, we provide upper and lower bounds on the smallest achievable gain from disturbances to the point-wise estimation error. The bounds rely on forward and backward Riccati recursions-one forward recursion for each feasible model and one backward recursion for each pair of feasible models. We give simple examples where the lower and upper bounds are tight.
- Abstract (Swedish)
- Artikeln behandlar fundamentala prestandabegränsningar för strikt kausala tillståndsskattningar hos linjära system som beskrivs av ett ändligt antal modeller. Systemet antas beskrivas korrekt av en av modellerna, men det är okänt vilken av kandidaterna som är korrekt. Vi ger metoder för att beräkna övre- och undre begränsningar på minimax prestanda, defininerad som förstärkningen från störningar till punktvis skattningsfel. Begränsningarna beräknas genom Riccatirecursioner som går framåt och bakåt i tiden.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/ebb0be74-2c03-46cb-89f4-298b5ef80890
- author
- Kjellqvist, Olle
LU
- organization
- alternative title
- Fundamentala minimax begränsningar för skattning med flera modeller
- publishing date
- 2024-03-28
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- in press
- subject
- host publication
- 2024 European Control Conference (ECC)
- pages
- 6 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- external identifiers
-
- scopus:85200544417
- ISBN
- 979-8-3315-4092-0
- 978-3-9071-4410-7
- DOI
- 10.23919/ECC64448.2024.10590947
- project
- Scalable Control using Learning and Adaptation
- language
- English
- LU publication?
- yes
- id
- ebb0be74-2c03-46cb-89f4-298b5ef80890
- alternative location
- https://arxiv.org/abs/2312.05159
- date added to LUP
- 2024-08-28 10:03:23
- date last changed
- 2024-09-13 09:54:01
@inproceedings{ebb0be74-2c03-46cb-89f4-298b5ef80890,
abstract = {{This article concerns the performance limits of strictly causal state estimation for linear systems with fixed, but uncertain, parameters belonging to a finite set. In particular, we provide upper and lower bounds on the smallest achievable gain from disturbances to the point-wise estimation error. The bounds rely on forward and backward Riccati recursions-one forward recursion for each feasible model and one backward recursion for each pair of feasible models. We give simple examples where the lower and upper bounds are tight.}},
author = {{Kjellqvist, Olle}},
booktitle = {{2024 European Control Conference (ECC)}},
isbn = {{979-8-3315-4092-0}},
language = {{eng}},
month = {{03}},
pages = {{2540--2546}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
title = {{Minimax Performance Limits for Multiple-Model Estimation}},
url = {{http://dx.doi.org/10.23919/ECC64448.2024.10590947}},
doi = {{10.23919/ECC64448.2024.10590947}},
year = {{2024}},
}