Simultaneous input and state estimation of linear discrete-time stochastic systems with input aggregate information

Sze Zheng Yong; Minghui Zhu; Emilio Frazzoli

doi:10.1109/CDC.2015.7402243

Simultaneous input and state estimation of linear discrete-time stochastic systems with input aggregate information

Sze Zheng Yong, Minghui Zhu, Emilio Frazzoli

Electrical Engineering

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

3 Scopus citations

Abstract

In this paper, we present filtering algorithms for simultaneous input and state estimation of linear discrete-time stochastic systems when the unknown inputs are partially known, i.e., when some aggregate information of the unknown inputs is available as linear equality or inequality constraints. The stability and optimality properties of the filters are presented and proven using two complementary perspectives. Specifically, we confirm the intuition that the partial input information improves the performance of the filters when a linear input equality constraint is given. On the other hand, given a linear inequality constraint, we show that the estimate error covariance is decreased but the estimates may be biased.

Original language	English (US)
Title of host publication	54rd IEEE Conference on Decision and Control,CDC 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	461-467
Number of pages	7
ISBN (Electronic)	9781479978861
DOIs	https://doi.org/10.1109/CDC.2015.7402243
State	Published - Feb 8 2015
Event	54th IEEE Conference on Decision and Control, CDC 2015 - Osaka, Japan Duration: Dec 15 2015 → Dec 18 2015

Publication series

Name	Proceedings of the IEEE Conference on Decision and Control
Volume	54rd IEEE Conference on Decision and Control,CDC 2015
ISSN (Print)	0743-1546
ISSN (Electronic)	2576-2370

Other

Other	54th IEEE Conference on Decision and Control, CDC 2015
Country/Territory	Japan
City	Osaka
Period	12/15/15 → 12/18/15

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
Modeling and Simulation
Control and Optimization

Access to Document

10.1109/CDC.2015.7402243

Cite this

Yong, S. Z., Zhu, M., & Frazzoli, E. (2015). Simultaneous input and state estimation of linear discrete-time stochastic systems with input aggregate information. In 54rd IEEE Conference on Decision and Control,CDC 2015 (pp. 461-467). Article 7402243 (Proceedings of the IEEE Conference on Decision and Control; Vol. 54rd IEEE Conference on Decision and Control,CDC 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CDC.2015.7402243

@inproceedings{c150ac6107f84d42958f2258efb8700c,

title = "Simultaneous input and state estimation of linear discrete-time stochastic systems with input aggregate information",

abstract = "In this paper, we present filtering algorithms for simultaneous input and state estimation of linear discrete-time stochastic systems when the unknown inputs are partially known, i.e., when some aggregate information of the unknown inputs is available as linear equality or inequality constraints. The stability and optimality properties of the filters are presented and proven using two complementary perspectives. Specifically, we confirm the intuition that the partial input information improves the performance of the filters when a linear input equality constraint is given. On the other hand, given a linear inequality constraint, we show that the estimate error covariance is decreased but the estimates may be biased.",

author = "Yong, {Sze Zheng} and Minghui Zhu and Emilio Frazzoli",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 54th IEEE Conference on Decision and Control, CDC 2015 ; Conference date: 15-12-2015 Through 18-12-2015",

year = "2015",

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day = "8",

doi = "10.1109/CDC.2015.7402243",

language = "English (US)",

series = "Proceedings of the IEEE Conference on Decision and Control",

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Yong, SZ, Zhu, M & Frazzoli, E 2015, Simultaneous input and state estimation of linear discrete-time stochastic systems with input aggregate information. in 54rd IEEE Conference on Decision and Control,CDC 2015., 7402243, Proceedings of the IEEE Conference on Decision and Control, vol. 54rd IEEE Conference on Decision and Control,CDC 2015, Institute of Electrical and Electronics Engineers Inc., pp. 461-467, 54th IEEE Conference on Decision and Control, CDC 2015, Osaka, Japan, 12/15/15. https://doi.org/10.1109/CDC.2015.7402243

Simultaneous input and state estimation of linear discrete-time stochastic systems with input aggregate information. / Yong, Sze Zheng; Zhu, Minghui; Frazzoli, Emilio.
54rd IEEE Conference on Decision and Control,CDC 2015. Institute of Electrical and Electronics Engineers Inc., 2015. p. 461-467 7402243 (Proceedings of the IEEE Conference on Decision and Control; Vol. 54rd IEEE Conference on Decision and Control,CDC 2015).

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

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AU - Frazzoli, Emilio

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Y1 - 2015/2/8

N2 - In this paper, we present filtering algorithms for simultaneous input and state estimation of linear discrete-time stochastic systems when the unknown inputs are partially known, i.e., when some aggregate information of the unknown inputs is available as linear equality or inequality constraints. The stability and optimality properties of the filters are presented and proven using two complementary perspectives. Specifically, we confirm the intuition that the partial input information improves the performance of the filters when a linear input equality constraint is given. On the other hand, given a linear inequality constraint, we show that the estimate error covariance is decreased but the estimates may be biased.

AB - In this paper, we present filtering algorithms for simultaneous input and state estimation of linear discrete-time stochastic systems when the unknown inputs are partially known, i.e., when some aggregate information of the unknown inputs is available as linear equality or inequality constraints. The stability and optimality properties of the filters are presented and proven using two complementary perspectives. Specifically, we confirm the intuition that the partial input information improves the performance of the filters when a linear input equality constraint is given. On the other hand, given a linear inequality constraint, we show that the estimate error covariance is decreased but the estimates may be biased.

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M3 - Conference contribution

AN - SCOPUS:84962022983

T3 - Proceedings of the IEEE Conference on Decision and Control

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EP - 467

BT - 54rd IEEE Conference on Decision and Control,CDC 2015

PB - Institute of Electrical and Electronics Engineers Inc.

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Yong SZ, Zhu M, Frazzoli E. Simultaneous input and state estimation of linear discrete-time stochastic systems with input aggregate information. In 54rd IEEE Conference on Decision and Control,CDC 2015. Institute of Electrical and Electronics Engineers Inc. 2015. p. 461-467. 7402243. (Proceedings of the IEEE Conference on Decision and Control). doi: 10.1109/CDC.2015.7402243

Simultaneous input and state estimation of linear discrete-time stochastic systems with input aggregate information

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