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

Sze Yong, Minghui Zhu, Emilio Frazzoli

Research output: Contribution to journalConference article

1 Citation (Scopus)

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 languageEnglish (US)
Article number7040044
Pages (from-to)4204-4209
Number of pages6
JournalProceedings of the IEEE Conference on Decision and Control
Volume2015-February
Issue numberFebruary
DOIs
StatePublished - Jan 1 2014
Externally publishedYes
Event2014 53rd IEEE Annual Conference on Decision and Control, CDC 2014 - Los Angeles, United States
Duration: Dec 15 2014Dec 17 2014

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Unknown Inputs
Stochastic systems
Discrete-time Systems
Stochastic Systems
Maximum likelihood
Smoothing
Smoothing Algorithm
Discrete-time Linear Systems
Interval
Gaussian Noise
Mean Squared Error
Maximum Likelihood
Entire
Numerical Examples
Estimate
Demonstrate
Observation

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization

Cite this

Simultaneous input and state smoothing for linear discrete-time stochastic systems with unknown inputs. / Yong, Sze; Zhu, Minghui; Frazzoli, Emilio.

In: Proceedings of the IEEE Conference on Decision and Control, Vol. 2015-February, No. February, 7040044, 01.01.2014, p. 4204-4209.

Research output: Contribution to journalConference article

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