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    Area of Science:

    • Control Systems Engineering
    • Signal Processing
    • Networked Systems

    Background:

    • Binary sensor networks present challenges in estimation due to limited information and unknown-but-bounded (UBB) noise.
    • Limited bit rates in network communication further complicate data fusion from distributed sensors.
    • Existing fusion methods may struggle with the inherent uncertainties and constraints of binary sensor networks.

    Purpose of the Study:

    • To develop a zonotopic distributed fusion estimation algorithm for general nonlinear systems using binary sensor networks.
    • To address challenges posed by UBB noises and limited bit-rate communication.
    • To enhance estimation accuracy and ensure reliable data decoding from individual sensor nodes.

    Main Methods:

    • A modified innovation approach is proposed to improve estimation accuracy with binary sensor data.
    • A novel coding-decoding strategy is introduced for reliable information retrieval at the fusion center.
    • A distributed fusion algorithm based on matrix weighting and zonotopic set-membership filtering is developed.
    • Optimization problems are solved to determine bit rate allocation and weighting coefficients.

    Main Results:

    • The F-radius of local zonotopic sets is derived and minimized by optimizing filtering gain parameters.
    • A sufficient condition is established to guarantee the uniform boundedness of the fused zonotopic F-radius.
    • The algorithm demonstrates improved estimation accuracy and robustness against UBB noises.

    Conclusions:

    • The proposed zonotopic distributed fusion estimation algorithm effectively handles nonlinear systems with binary sensors under UBB noise and limited bandwidth.
    • The developed coding-decoding approach and optimization strategies ensure reliable and accurate state estimation.
    • The algorithm's practical utility is validated through application to ballistic object tracking systems.