Indoor passive localization and tracking: From theory to practice

Sun, Jiachen; Ding, Guoru; Zheng, Ze; Zhou, Huaji

doi:10.1109/wcsp.2016.7752559

Cited by 2 publications

(1 citation statement)

References 7 publications

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“…The RSSI-based technology realizes localization by characterizing the energy attenuation of wireless signals during propagation, which is readily available from commodity WiFi. Several methods combine the RSSI readings from multiple access points (APs) with propagation models and then locate the persons through triangulation [18] or fingerprint collection [19], [20]. However, since RSSI is a coarse characterization of the channel, reliable RSSI-based positioning systems tend to achieve only room-level accuracy.…”

Section: A Passive Indoor Localizationmentioning

confidence: 99%

Multi-Person Passive WiFi Indoor Localization with Intelligent Reflecting Surface

Zhang¹,

Zhang²,

He³

et al. 2022

Preprint

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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.

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Section: A Passive Indoor Localizationmentioning

confidence: 99%

Multi-Person Passive WiFi Indoor Localization with Intelligent Reflecting Surface

Zhang¹,

Zhang²,

He³

et al. 2022

Preprint

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show abstract

Multi-Person Passive WiFi Indoor Localization With Intelligent Reflecting Surface

Zhang

et al. 2023

IEEE Trans. Wireless Commun.

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This paper studies a multiple intelligent reflecting surfaces (IRSs) collaborative localization system where multiple semi-passive IRSs are deployed in the network to locate one or more targets based on time-of-arrival. It is assumed that each semi-passive IRS is equipped with reflective elements and sensors, which are used to establish the line-of-sight links from the base station (BS) to multiple targets and process echo signals, respectively. Based on the above model, we derive the Fisher information matrix of the echo signal with respect to the time delay. By employing the chain rule and exploiting the geometric relationship between time delay and position, the Cramér-Rao bound (CRB) for estimating the target's Cartesian coordinate position is derived. Then, we propose a two-stage algorithmic framework to minimize CRB in single-and multi-target localization systems by joint optimizing active beamforming at BS, passive beamforming at multiple IRSs and IRS selection. For the single-target case, we derive the optimal closed-form solution for multiple IRSs coefficients design and propose a lowcomplexity algorithm based on alternating direction method of multipliers to obtain the optimal solution for active beaming design. For the multi-target case, alternating optimization is used to transform the original problem into two subproblems where semi-definite relaxation and successive convex approximation are applied to tackle the quadraticity and indefiniteness in the CRB expression, respectively. Finally, numerical simulation results validate the effectiveness of the proposed algorithm for multiple IRSs collaborative localization system compared to other benchmark schemes as well as the significant performance gains.

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Indoor passive localization and tracking: From theory to practice

Cited by 2 publications

References 7 publications

Multi-Person Passive WiFi Indoor Localization with Intelligent Reflecting Surface

Multi-Person Passive WiFi Indoor Localization with Intelligent Reflecting Surface

Multi-Person Passive WiFi Indoor Localization With Intelligent Reflecting Surface

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