A TDoA based Positioning Method for Wireless Networks assisted by Passive RIS

Liu, Ruiqi; Jian, Mengnan; Zhang, Wei

doi:10.1109/gcwkshps56602.2022.10008550

Cited by 5 publications

(2 citation statements)

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“…where  is a small positive number used to avoid a zero denominator. After obtaining the corresponding weights of the coordinates of the S reference points, the estimated coordinates US LOC of US can be obtained using (36). The localization error e was calculated using the Euclidean distance between the estimated coordinates of US and true coordinates of US , as expressed by (37).…”

Section:  mentioning

confidence: 99%

“…This ensures that it propagates in the direction of the base station and is eventually received by the base station [35]. Authors of [36] studied the challenges of having fully passive IRSs in the network to localization, and demonstrated that with sufficient number of IRSs, the conventional positioning protocol could still function normally with a satisfying accuracy. In [37], a near-field localization algorithm using a large linear IRS in an NLOS environment was used to improve the signal coverage, and the results show that the method can provide good localization accuracy even at high obstruction rates.…”

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confidence: 99%

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Fingerprint Based Localization Enabled by Low-Rank Matrix Reconstruction in Intelligent Reflective Surface Assisted Networks

duan,

zhang,

Liu

2024

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The intelligent Reflective Surface (IRS) is a novel network node that consists of a large-scale passive reflective array to obtain customized reflected wave direction by modulating the amplitude, phase, which can be easy deployed to change the wireless signal propagation environment and enhance the communication performance under Non-Line-of Sight (NLOS) environment where location services cannot be performed accurately. In this paper, a low-rank matrix reconstruction enabled fingerprint-based localization algorithm for IRS-assisted networks is proposed. Firstly, a 5G positioning system based on IRSs is constructed using multiple IRSs deployed to reflect signals. This enables the base station to overcome the influence of NLOS and thus receive the positioning signal of the point to be positioned. Then, the angular domain power expectation matrix of the received signal is extracted as fingerprint to form a partial fingerprint database. As a next step, the complete fingerprint database is reconstructed using the low-rank matrix fitting algorithm, thereby considerably reducing the workload of building the fingerprint database. Finally, maximal ratio combining is used to increase the gap between the fingerprint data, and the Weighted K-Nearest Neighbor (WKNN) algorithm is used to match the fingerprint data and estimate the location of the points to be located. The simulation results demonstrate the feasibility of the proposed method to achieve a sub-meter accuracy in a NLOS environment.

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Section:  mentioning

confidence: 99%

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confidence: 99%