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    This study introduces a distributed iterative finite impulse response (DIFIR) consensus filter for target tracking when full target state information is unavailable. The novel filter achieves robust H-infinity consensus for leader-following systems, even with unknown leader inputs.

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    Area of Science:

    • Control Systems Engineering
    • Robotics
    • Distributed Systems

    Background:

    • Target tracking often faces challenges with incomplete state information due to cost or impossibility of direct measurement.
    • Distributed methods are crucial for robustness and resource efficiency in multi-agent systems where direct target measurement is infeasible for all agents.

    Purpose of the Study:

    • To develop a distributed iterative finite impulse response (DIFIR) consensus filter for leader-following systems.
    • To address target tracking problems with limited or unavailable full state information.
    • To enhance robustness and reduce communication/computation overhead in multi-agent systems.

    Main Methods:

    • A distributed measurement model is employed, integrating neighbors' estimates and directed measurement data.
    • A computationally efficient iterative algorithm is utilized for state estimation.
    • The approach is extended to handle leaders with unknown inputs by incorporating input estimation.

    Main Results:

    • The proposed DIFIR filter achieves H-infinity consensus, where followers obtain unbiased estimates of the leader's state.
    • The filter demonstrates consistency and robustness in leader-following scenarios.
    • The developed techniques are validated through illustrative examples.

    Conclusions:

    • The DIFIR consensus filter offers an effective solution for distributed target tracking with incomplete information.
    • The method enhances system robustness and efficiency in leader-following and multi-agent systems.
    • The proposed approach provides a reliable framework for state estimation in complex dynamic environments.