PRSRTI: A Novel Device-Free Localization Method Using Phase Response Shift Based Radio Tomography Imaging

Ma, Yongtao; Wang, Bobo; Ning, Wanru; Zhang, Yunlei

doi:10.1109/tvt.2020.3027957

Cited by 12 publications

(3 citation statements)

References 32 publications

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“…The grid of the circle stay is denoted as d 58 , and for the other columns of the grid, the closer the circle is, the stronger the similarity. For example, the coordinates of triangles and squares are [4,7] and [7,5], which represent d 47 and d 75 , respectively, according to the equation above. The color around the triangles is lighter than the color surrounding the squares, which means a stronger similarity due to the shorter distance.…”

Section: Quantitative Indexmentioning

confidence: 99%

“…However, it is worth noting that the above two traditional positioning methods are no longer applicable when the target we want to locate is not able to carry a satellite signal receiving device, or when the target itself does not want to be monitored and located by satellite [3]. From the perspective of intelligent rail transportation, on the one hand, signal sensing and device-free positioning are the fundamental means to ensure the rapid development of intelligent transportation; on the other hand, from the perspective of security monitoring [4], it is a serious challenge for the intelligent rail transportation field to quickly determine emergency situations and respond to potential threats.…”

Section: Introductionmentioning

confidence: 99%

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Meta-Heuristic Device-Free Localization Algorithm under Multiple Path Effect

Guo

et al. 2023

Entropy

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In the scenario of device-free localization under multiple effects, the accuracy of localization based on compressed sensing theory is severely affected. Most existing localization techniques directly ignore multiple path effects. However, it is not practical to ignore the multiple path effect due to its high signal strength, which can provide localization information. In this paper, we formulate the sensing matrix optimization problem in compressed sensing for device-free localization scenarios based on multiple reflections. To solve this problem, we model it as a constrained combinatorial optimization problem and propose a hybrid meta-heuristic algorithm. First, smart reflection surfaces and virtual node models are used to construct the desired communication links. Second, we iteratively improve the properties of the measurement matrix by using K-means clustering to obtain reasonable thresholds, and use a meta-heuristic algorithm to optimize the sensing matrix. Finally, the simulation results show that the proposed method efficiently optimizes the sensing matrix and achieves fast and high-precision localization while conserving communication resources.

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Section: Quantitative Indexmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Meta-Heuristic Device-Free Localization Algorithm under Multiple Path Effect

Guo

et al. 2023

Entropy

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“…In [11], target localization with the one-bit analog-to-digital-converter (ADC) is proposed for the internet-of-things application. For the device-free localization, authors of [12] used the phase response shift to localize targets and improve the localization performance. Recently, reconfigurable intelligent surface (RIS) has been adopted in wireless communication for controlled reconfiguration of communication environment [13,14].…”

Section: Introductionmentioning

confidence: 99%

A RIS-Based Passive DOA Estimation Method for Integrated Sensing and Communication System

Chen¹,

Chen²,

Wang³

2022

Preprint

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Integrated sensing and communication (ISAC) system has received growing attention, especially in the context of B5G/6G development. Combining the reconfigurable intelligent surface (RIS) with wireless communication process, a novel passive sensing technique is formulated in this paper to estimate the direction of arrival (DOA) of the targets, where the control matrix of the RIS is used to to realize the multiple measurements with only one full-functional receiving channel. Unlike the existing methods, the interference signals introduced by wireless communication are also considered. To improve the DOA estimation, a novel atomic norm-based method is proposed to remove the interference signals by the sparse reconstruction. The DOA is estimated after the interference removal by a novel Hankel-based multiple signal classification (MUSIC) method. Then, an optimization method is also developed for the measurement matrix to reduce the power interference signals and keep the measurement matrix's randomness, which guarantees the performance of the sparse reconstruction. Finally, we derive the theoretical Cramér-Rao lower bound (CRLB) for the proposed system on the DOA estimation. Simulation results show that the proposed method outperforms the existing methods in the DOA estimation and shows the corresponding CRLB with different distributions of the sensing node. The code about the proposed method is available online https://github.com/chenpengseu/ PassiveDOA-ISAC-RIS.git.

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