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  • Minimal Number of Sensor Nodes for Distributed Kalman Filtering

    Author(s)
    Li, Wangyan
    Yang, Fuwen
    Thiel, David V
    Wei, Guoliang
    Griffith University Author(s)
    Thiel, David V.
    Yang, Fuwen
    Li, Wangyan
    Year published
    2020
    Metadata
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    Abstract
    Finding and identifying the minimal number of sensor nodes for a sensor network is one of the most basic problems for the implementation of distributed state estimators. Despite a plethora of research studied sensor networks, most of them ignored this problem or assumed the considered sensor network comes with an ideal number of sensor nodes. We revisit this problem in the current paper. To this end, the minimal number of sensor nodes problem is first formalized and a novel observability condition, namely, minimal nodes uniform observability (MNUO), is then proposed. Next, this MNUO is applied to study the stability issues ...
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    Finding and identifying the minimal number of sensor nodes for a sensor network is one of the most basic problems for the implementation of distributed state estimators. Despite a plethora of research studied sensor networks, most of them ignored this problem or assumed the considered sensor network comes with an ideal number of sensor nodes. We revisit this problem in the current paper. To this end, the minimal number of sensor nodes problem is first formalized and a novel observability condition, namely, minimal nodes uniform observability (MNUO), is then proposed. Next, this MNUO is applied to study the stability issues of the distributed Kalman filtering algorithm. In what follows, under the condition of MNUO, conditions to ensure its stability are given and the results about the relation of the filtering performance before and after selecting the minimal number of sensor nodes are obtained. Finally, optimization solutions and an example are given to find the minimal number of sensor nodes for a sensor network.
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    Journal Title
    IEEE Transactions on Systems, Man, and Cybernetics: Systems
    DOI
    https://doi.org/10.1109/tsmc.2020.3034732
    Note
    This publication has been entered as an advanced online version in Griffith Research Online.
    Subject
    Artificial intelligence
    Publication URI
    http://hdl.handle.net/10072/400208
    Collection
    • Journal articles

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