SRAL-IRS: Swift, Robust, and Accurate IRS-aided Localization with COTS WiFi

Intelligent Reflecting Surface (IRS) has emerged as a crucial technology for indoor localization in future wireless networks. However, existing IRS-aided systems that utilize commercial off-the-shelf (COTS) WiFi devices face challenges due to two key factors. First, the passive reflective nature of IRS generates relatively weak reflected signals, which can be easily drowned out by multipath interference and noise. Secondly, existing works require a large number of IRS codebooks for fine-grained scanning of the entire area, resulting in significant time requirements. This paper introduces SRAL-IRS, which enables swift, robust, and accurate IRS-aided indoor localization using commercial WiFi devices. Through intelligent codebook design and theoretical derivation, we reveal the theoretical relationship between the IRS codebook modifications, target position, and variations in the received signal. Subsequently, SRAL-IRS mitigates the impact of environmental and hardware noise by carefully selecting and combining WiFi subcarriers. Finally, by utilizing the received data from multiple codebooks and subcarriers and leveraging the orthogonality between the noise subspace and the IRS-reflected signal subspace, we can achieve precise localization even with a limited number of IRS codebooks. Real-world implementation of SRAL-IRS, utilizing our designed IRS prototype and COTS WiFi devices, validates its feasibility and effectiveness.