Unmanned Aerial Vehicle (UAV) technology is increasingly gaining attention in localization systems due to its flexibility and mobility. However, traditional localization techniques often fail in complex environments where line-of-sight paths are obstructed. To address this challenge, this paper presents an innovative UAV-assisted high-precision multi-target localization system. The system utilizes UAVs equipped with Reconfigurable Intelligent Surfaces to create a reflective signal path, allowing a receiver sensor to capture these signals, creating favorable conditions for multi-target localization. Exploiting the sparsity of signals, we introduce a direct positioning algorithm that leverages Atomic Norm Minimization (ANM) to estimate the target’s location. To address the high complexity of traditional ANM methods, we propose a novel Coyote-ANM-based direct localization (CADL) approach. This method combines the coyote optimization algorithm with the alternating direction method of multipliers to achieve high-accuracy positioning with reduced computational complexity. Simulation results across various signal-to-noise ratio scenarios demonstrate that the proposed algorithm significantly improves localization accuracy, achieving lower root mean square error values and faster execution times compared to traditional methods.


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    Title :

    UAV-Mounted RIS-Aided Multi-Target Localization System: An Efficient Sparse-Reconstruction-Based Approach


    Contributors:
    Jingjing Li (author) / Jianhui Wang (author) / Weijia Cui (author) / Chunxiao Jian (author)


    Publication date :

    2024




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




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