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Outlier-Resistant Recursive Filtering for Multisensor Multirate Networked Systems Under Weighted Try-Once-Discard

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    This summary is machine-generated.

    This study introduces an outlier-resistant recursive filtering method for networked systems. The new filter ensures robust performance despite data outliers and network congestion, validated through simulations.

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    Area of Science:

    • Control Systems Engineering
    • Signal Processing
    • Networked Systems

    Background:

    • Multisensor multirate networked systems face challenges with data outliers and network congestion.
    • Traditional filtering methods can be compromised by noisy sensor data and limited communication bandwidth.

    Purpose of the Study:

    • To develop a novel outlier-resistant recursive filtering (RF) scheme for multisensor multirate networked systems.
    • To address challenges posed by measurement outliers and network communication protocols like weighted try-once-discard (WTOD).

    Main Methods:

    • Implemented a saturation function within the filter structure to mitigate the impact of measurement outliers.
    • Utilized matrix difference equations to derive an upper bound for the filtering error covariance.
    • Characterized the filter gain matrix to minimize the derived upper bound.
    • Analyzed the exponential boundedness of filtering error dynamics in the mean square sense.

    Main Results:

    • An upper bound on the filtering error covariance was successfully obtained.
    • The optimal filter gain matrix was determined to minimize this upper bound.
    • The proposed filter demonstrated resilience against measurement outliers.
    • Exponential boundedness of the filtering error dynamics was confirmed.

    Conclusions:

    • The developed outlier-resistant recursive filtering scheme effectively handles measurement outliers in multisensor multirate networked systems.
    • The WTOD protocol integration and saturation function contribute to robust filtering performance under network constraints.
    • Simulation results validate the practical applicability and effectiveness of the proposed filtering approach.