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

Sze Zheng Yong, Minghui Zhu, Emilio Frazzoli

    Research output: Contribution to journalConference article

    1 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 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
    Event2014 53rd IEEE Annual Conference on Decision and Control, CDC 2014 - Los Angeles, United States
    Duration: Dec 15 2014Dec 17 2014

    All Science Journal Classification (ASJC) codes

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

    Fingerprint Dive into the research topics of 'Simultaneous input and state smoothing for linear discrete-time stochastic systems with unknown inputs'. Together they form a unique fingerprint.

  • Cite this