On the Performance Gain of Harnessing Non-Line-of-Sight Propagation for Visible Light-Based Positioning

Zhou, Bingpeng; Zhuang, Yuan; Cao, Yue

doi:10.1109/twc.2020.2988001

Cited by 21 publications

(6 citation statements)

References 45 publications

(97 reference statements)

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“…However, these SPAO methods [10]- [19], [22]- [27] consider a line-of-sight (LOS) scenario, and hence scattering interference and random fading are unresolved, which are major error sources that cannot be safely ignored in VLP [28]- [33].…”

Section: A Research Motivationmentioning

confidence: 99%

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6-DoF Location-and-Pose Estimation Toward Integrated Visible Light Communication and Sensing: Algorithm Design and Performance Limits

Zhou,

Wang,

Shen

et al. 2024

IEEE Trans. Signal Process.

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Visible light communication (VLC) is envisioned as an important technique for short-range communications, but its potential for user device (UD) localization is not fully exploited, which is challenging due to diffuse-scattering interference and random fading. In this paper, we focus on the 6 degree-of-freedom (DoF) UD state estimation (i.e., 3D location and 3D pose angles) based on VLC. Specifically, a novel successive convex approximation (SCA)-based optimization algorithm is proposed, where UD location, pose angles and channel state are simultaneously estimated. Thus, the disturbance of random fading and diffusescattering interference for UD localization is alleviated via VLCassisted channel equalization, and hence our SCA-based 6-DoF state detection solution outperforms state-of-the-art baselines. In addition, a unified performance analysis framework is established for VLC-based 6-DoF state detection, and structured information models are exploited for tractable performance quantification. A closed-form Cramer-Rao lower bound on UD localization error and pose angle estimate error is established respectively, and the impact of measurement noise, scattering interference, bandwidth, the quantities of photodiodes and light-emitting-diodes, etc, on the VLC-based UD state detection performance is analysed. Our closed-form analysis cannot only shed lights on the performance limits of VLC-based 6D state detection, but also gain insights into the impact of system configuration and fading environments.

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Section: A Research Motivationmentioning

confidence: 99%

“…In summary, it is concluded in [24] and [28] that geometric intersection and LRM-driven VLP performance will be seriously degraded by scattering interference, especially in a high signal-to-noise ratio (SNR) environment. Moreover, VLP also suffers from random fading due to uncertain reflection rate and variant environments.…”

Section: A Research Motivationmentioning

confidence: 99%

6-DoF Location-and-Pose Estimation Toward Integrated Visible Light Communication and Sensing: Algorithm Design and Performance Limits

Zhou,

Wang,

Shen

et al. 2024

IEEE Trans. Signal Process.

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“…This is mainly due to the fact that the expression of the NLOS channel gain w.r.t the UE location and orientation is complex, and hence, it could not be handled straightforwardly in an optimization fashion. However, it was shown recently in [32] that LiFi systems can gain additional UE position and orientation information from the NLOS links via leveraging the NLOS propagation knowledge. Specifically, the closed-form Cramer-Raw lower bounds (CRLBs) on the estimation errors of the UE location and orientation, are derived.…”

Section: B Existing Solutionsmentioning

confidence: 99%

Invoking Deep Learning for Joint Estimation of Indoor LiFi User Position and Orientation

Arfaoui

Soltani

Tavakkolnia

et al. 2021

IEEE J. Select. Areas Commun.

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“…To minimize errors, Lekkas et al employed statistical testing to assess the quality distribution of observed values, thus improving positioning accuracy [21]. Furthermore, nonlinear iterative methods include Fang, SX, SI, and Taylor series expansions [22,23]. To reduce the location bias and improve location accuracy, Liang et al presented a novel bias-reduced method based on an iterative constrained weighted least squares algorithm [24].…”

Section: Introductionmentioning

confidence: 99%

Development of Hydroacoustic Localization Algorithms for AUV Based on the Error-Corrected WMChan-Taylor Algorithm

Yang,

Gao,

et al. 2024

JMSE

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Autonomous underwater vehicles (AUVs) are susceptible to non-line-of-sight (NLOS) errors and noise bias at receiving stations during the application of hydroacoustic localization systems, leading to a degradation in positioning accuracy. To address this problem, this paper optimizes the Chan-Taylor algorithm. Initially, we propose the Weighted Modified Chan-Taylor (WMChan-Talor) algorithm, which introduces dynamic weights into the Chan algorithm to correct noise variance at measurement stations, thereby improving the accuracy of AUV positioning. Computer simulations validate the effectiveness of the WMChan-Taylor algorithm in enhancing positioning accuracy. To further address the accuracy degradation caused by noise deviations across different receiving stations, we introduce an error-corrected WMChan-Taylor algorithm. This algorithm utilizes a standard residual function to eliminate significant delays caused by large errors at receiving stations and applies standard residual weighting to improve the combined positioning solution. The performance of the error-corrected WMChan-Taylor algorithm is demonstrated through both computer and semi-physical simulation experiments, confirming its capability to isolate noisier stations and thus enhance overall positioning accuracy.

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On the Performance Gain of Harnessing Non-Line-of-Sight Propagation for Visible Light-Based Positioning

Cited by 21 publications

References 45 publications

6-DoF Location-and-Pose Estimation Toward Integrated Visible Light Communication and Sensing: Algorithm Design and Performance Limits

6-DoF Location-and-Pose Estimation Toward Integrated Visible Light Communication and Sensing: Algorithm Design and Performance Limits

Invoking Deep Learning for Joint Estimation of Indoor LiFi User Position and Orientation

Development of Hydroacoustic Localization Algorithms for AUV Based on the Error-Corrected WMChan-Taylor Algorithm

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