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

被引:0
作者
Li, Jingjing [1 ]
Wang, Jianhui [1 ]
Cui, Weijia [1 ]
Jian, Chunxiao [1 ]
机构
[1] Informat Engn Univ, Sch Informat & Commun Engn, Zhengzhou 450001, Peoples R China
基金
中国国家自然科学基金;
关键词
reflecting intelligent surface; Atomic Norm Minimization; direct localization; alternating direction method of multipliers; passive sensing; DIRECT POSITION DETERMINATION; RECONFIGURABLE INTELLIGENT SURFACES; MIMO;
D O I
10.3390/drones8110694
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
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.
引用
收藏
页数:20
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