Multi-Person Passive WiFi Indoor Localization with Intelligent Reflecting Surface
원문 초록
The past years have witnessed increasing research interest in achieving passive human localization with commodity WiFi devices. However, due to the fundamental limited spatial resolution of WiFi signals, it is still very difficult to achieve accurate localization with existing commodity WiFi devices. To tackle this problem, in this paper, we propose to exploit the degree of freedom provided by the Intelligent Reflecting Surface (IRS), which is composed of a large number of controllable reflective elements, to modulate the spatial distribution of WiFi signals and thus break down the spatial resolution limitation of WiFi signals to achieve accurate localization. Specifically, in the single-person scenario, we derive the closed-form solution to optimally control the phase shift of the IRS elements. In the multi-person scenario, we propose a Side-lobe Cancellation Algorithm to eliminate the near-far effect to achieve accurate localization of multiple persons in an iterative manner. Extensive simulation results demonstrate that without any change to the existing WiFi infrastructure, the proposed framework can locate multiple moving persons passively with sub-centimeter accuracy under multipath interference and random noise.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
EnviKal-Loc: Sub-10m Indoor LoRaWAN Localization using an Environmental-Aware Path Loss and Adaptive RSSI Smoothing
A Statistical Evaluation of Indoor LoRaWAN Environment-Aware Propagation for 6G: MLR, ANOVA, and Residual Distribution Analysis
Visual Re-Ranking with Non-Visual Side Information
AI-Driven Relocation Tracking in Dynamic Kitchen Environments
PALMS: Plane-based Accessible Indoor Localization Using Mobile Smartphones
wav2pos: Sound Source Localization using Masked Autoencoders
토론
이 논문과 연결된 포럼 글이 아직 없습니다.