Simultaneous input and state smoothing for linear discrete-time stochastic systems with unknown inputs

Sze Yong; Minghui Zhu; Emilio Frazzoli

doi:10.1109/CDC.2014.7040044

Simultaneous input and state smoothing for linear discrete-time stochastic systems with unknown inputs

Sze Yong, Minghui Zhu, Emilio Frazzoli

Research output: Contribution to journal › Conference article › peer-review

3 Scopus citations

Abstract

This paper considers the problem of simultaneously estimating the states and unknown inputs of linear discrete-time systems in the presence of additive Gaussian noise based on observations from the entire time interval. A fixed-interval input and state smoothing algorithm is proposed for this problem and the input and state estimates are shown to be unbiased and to achieve minimum mean squared error and maximum likelihood. A numerical example is included to demonstrate the performance of the smoother.

Original language	English (US)
Article number	7040044
Pages (from-to)	4204-4209
Number of pages	6
Journal	Proceedings of the IEEE Conference on Decision and Control
Volume	2015-February
Issue number	February
DOIs	https://doi.org/10.1109/CDC.2014.7040044
State	Published - Jan 1 2014
Externally published	Yes
Event	2014 53rd IEEE Annual Conference on Decision and Control, CDC 2014 - Los Angeles, United States Duration: Dec 15 2014 → Dec 17 2014

ASJC Scopus subject areas

Control and Systems Engineering
Modeling and Simulation
Control and Optimization

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10.1109/CDC.2014.7040044

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title = "Simultaneous input and state smoothing for linear discrete-time stochastic systems with unknown inputs",

abstract = "This paper considers the problem of simultaneously estimating the states and unknown inputs of linear discrete-time systems in the presence of additive Gaussian noise based on observations from the entire time interval. A fixed-interval input and state smoothing algorithm is proposed for this problem and the input and state estimates are shown to be unbiased and to achieve minimum mean squared error and maximum likelihood. A numerical example is included to demonstrate the performance of the smoother.",

author = "Sze Yong and Minghui Zhu and Emilio Frazzoli",

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AU - Frazzoli, Emilio

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N2 - This paper considers the problem of simultaneously estimating the states and unknown inputs of linear discrete-time systems in the presence of additive Gaussian noise based on observations from the entire time interval. A fixed-interval input and state smoothing algorithm is proposed for this problem and the input and state estimates are shown to be unbiased and to achieve minimum mean squared error and maximum likelihood. A numerical example is included to demonstrate the performance of the smoother.

AB - This paper considers the problem of simultaneously estimating the states and unknown inputs of linear discrete-time systems in the presence of additive Gaussian noise based on observations from the entire time interval. A fixed-interval input and state smoothing algorithm is proposed for this problem and the input and state estimates are shown to be unbiased and to achieve minimum mean squared error and maximum likelihood. A numerical example is included to demonstrate the performance of the smoother.

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ER -

Simultaneous input and state smoothing for linear discrete-time stochastic systems with unknown inputs

Abstract

ASJC Scopus subject areas

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