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    This study introduces a distributed Kalman filter for interconnected systems, using local and neighbor data. A decoupling strategy ensures accurate estimation and stable performance, validated in a vehicle platoon system.

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    Area of Science:

    • Control Systems Engineering
    • Estimation Theory
    • Networked Systems

    Background:

    • Interconnected dynamic systems present challenges for traditional centralized estimation.
    • Distributed estimation requires balancing local information with inter-system dependencies.
    • Kalman filtering is a cornerstone for optimal state estimation in dynamic systems.

    Purpose of the Study:

    • To develop a distributed Kalman filter for interconnected dynamic systems.
    • To minimize the impact of system interconnections on estimation accuracy.
    • To ensure the stability and boundedness of the distributed estimator.

    Main Methods:

    • A decoupling strategy is employed to mitigate the effects of interconnected terms.
    • A recursive, distributed Kalman filter is derived using the minimum mean-squared error criterion.
    • Lyapunov stability criteria for linear time-varying systems are used to guarantee estimator boundedness.

    Main Results:

    • The proposed decoupling strategy effectively minimizes estimation performance degradation due to interconnections.
    • A stable and bounded distributed Kalman filter is successfully derived.
    • The recursive filter achieves optimal performance in the minimum mean-squared error sense.

    Conclusions:

    • The developed distributed Kalman filter is effective for interconnected dynamic systems.
    • The decoupling strategy enhances estimation robustness and stability.
    • The method's efficacy is demonstrated through simulations on a heavy-duty vehicle platoon system.