Generalized innovation and inference algorithms for hidden mode switched linear stochastic systems with unknown inputs

Sze Zheng Yong, Minghui Zhu, Emilio Frazzoli

    Research output: Contribution to journalConference article

    10 Scopus citations

    Abstract

    In this paper, we propose inference algorithms for simultaneously estimating the mode, input and state of hidden mode switched linear stochastic systems with unknown inputs. First, we define the generalized innovation for the recently proposed optimal filter for simultaneous input and state estimation [1] and show that the sequence is a Gaussian white noise. Then, we utilize this whiteness property of the generalized innovation, which reflects the estimation quality to form the likelihood function of the system model. Consequently, we employ the multiple model (MM) approach based on the likelihood function for inferring the hidden mode of switched linear stochastic systems. Algorithms for both static and dynamic MM estimation are presented and compared using a simulation example of vehicles at an intersection with switching driver intentions.

    Original languageEnglish (US)
    Article number7039914
    Pages (from-to)3388-3394
    Number of pages7
    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

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