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Multipath-Assisted Single-Anchor Localization via Deep Variational Learning
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DOI：10.1109/TWC.2024.3359047.png)
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摘要
En 中文
Location awareness plays an increasingly important role in wireless network applications. However, accurate localization in complex indoor environments remains challenging for existing radio frequency (RF)-based systems, among which the ultra-wide bandwidth (UWB) technology ranks to be the most promising one due to its capability in providing channel information with fine time resolution. In this paper, we propose a multipath-assisted single-anchor localization framework that can provide high-accuracy positional information in complex indoor environments. Specifically, a deep variational learning method is proposed to produce calibrated estimates of position-related parameters, including distance, time-difference-of-arrival and angle-of-arrival, which are then fed into a multipath-assisted single-anchor localization algorithm. The proposed method is implemented on self-built UWB transceivers and assessed with real-world data from an indoor measurement campaign. Extensive experimental results show that the proposed method outperforms conventional machine learning-based error mitigation approaches and can achieve 0.15m root mean square position error in non-line-of-sight scenarios.
Keyword:
Location awareness
Estimation
Nonlinear optics
Antenna arrays
Learning systems
Indoor environment
Bayes methods
Single-anchor localization
ultra-wide bandwidth
channel impulse response
multipath components
variational inference
deep learning
期刊
IF:
10.7
论文数:
1.3W
被引数:
5.3W
机构
引用论文
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IEEE ACCESS
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IEEE ACCESSIF3.6
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