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    Area of Science:

    • Control Systems Engineering
    • Signal Processing
    • Stochastic Systems

    Background:

    • Networked systems are susceptible to performance degradation due to measurement outliers.
    • Randomly occurring measurement outliers (ROMOs) pose a significant challenge in recursive filtering.
    • Existing filtering methods may struggle with the dynamic and unpredictable nature of ROMOs.

    Purpose of the Study:

    • To develop a novel recursive filtering algorithm for time-varying systems with ROMOs.
    • To accurately model the dynamical behaviors of ROMOs using stochastic methods.
    • To preserve filtering performance by actively detecting and removing outlier-corrupted measurements.

    Main Methods:

    • A new model for ROMOs using independent and identically distributed stochastic scalars.
    • A probabilistic encoding-decoding scheme for measurement signal conversion.
    • An active detection-based method to remove problematic measurements.
    • A recursive calculation approach to derive time-varying filter parameters by minimizing the filtering error covariance upper bound.
    • Stochastic analysis techniques to analyze the uniform boundedness of the filtering error covariance.

    Main Results:

    • A novel recursive filtering algorithm designed to mitigate the impact of ROMOs.
    • The proposed algorithm effectively removes measurements contaminated by outliers.
    • A time-varying filter parameter is derived recursively by minimizing the filtering error covariance.
    • The uniform boundedness of the filtering error covariance is theoretically analyzed.
    • Numerical examples demonstrate the effectiveness and correctness of the developed filter design.

    Conclusions:

    • The developed recursive filtering approach effectively addresses the challenge of ROMOs in networked time-varying systems.
    • The active detection and removal of outlier measurements ensure robust and reliable filtering.
    • The proposed method provides a significant advancement in maintaining filtering accuracy under noisy conditions.