使用NLOS toa或tdoa的基于鲁棒核的机器学习定位

2017 IEEE Long Island Systems, Applications and Technology Conference (LISAT) Pub Date : 2017-05-01 DOI:10.1109/LISAT.2017.8001981

Jun Li, I. Lu, Jonathan S. Lu, Lingwen Zhang

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引用次数: 8

摘要

提出了一种鲁棒的基于核的机器学习定位方案，利用到达时间(TOA)或到达时间差(TDOA)在非视距(NLOS)环境下进行定位。该方案可以在参考节点粗随机分布于感兴趣区域的情况下提供精确的位置估计。此外，该方案对随机TOA同步和测量误差不敏感。

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Robust kernel-based machine learning localization using NLOS TOAs or TDOAs

A robust kernel-based machine learning localization scheme using time of arrival (TOA) or time difference of arrival (TDOA) in none-line-of-sight (NLOS) environments is proposed. The scheme can provide accurate position estimation while the reference nodes are coarsely and randomly distributed in the area of interests. Moreover, the scheme is insensitive with respect to random TOA synchronization and measurement errors.

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来源期刊

2017 IEEE Long Island Systems, Applications and Technology Conference (LISAT)

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