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Full Space Source Sensing Method Assisted by STAR-RIS
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AI
DOI：10.1109/LWC.2026.3661292.png)
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Abstract
En 中文
Existing reconfigurable intelligent surfaces (RIS) assisted sensing methods fail to adequately consider mixed near-field (NF) and far-field (FF) environments. Consequently, this leads to a diminishment in sensing accuracy, particularly in large-scale RIS deployment scenarios. This letter proposes a full space source sensing method assisted by simultaneously transmitting and reflecting (STAR)-RIS. Specifically, a STAR-RIS-assisted unified NF and FF model is established. Subsequently, we reduce the model’s dimensionality by decoupling the transmission and reflection coefficients of STAR-RIS. Finally, a two-dimensional (2D) model-applicable newtonized orthogonal matching pursuit (NOMP) algorithm for STAR-RIS tailored to the mixed NF and FF scenario is proposed. Simulation results show that the proposed method can achieve full space source position sensing estimation with an accuracy of 1 mm.
Keywords:
Simultaneously transmitting and reflecting reconfigurable intelligent surface
DOA estimation
mixed far-field and near-field sources
NOMP
Journal
I
IF:
5.5
Papers:
663
Citations:
0