海报摘要:EIL——一种与环境无关的无设备被动定位方法

IPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks Pub Date : 2014-04-15 DOI:10.1109/IPSN.2014.6846768

Liqiong Chang, Dingyi Fang, Zhe Yang, Xiaojiang Chen, Ju Wang, Weike Nie, Tianzhang Xing

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引用次数: 1

摘要

以往的无设备被动定位(DFL)方法大多是基于学习的，它们假设被物体扭曲的接收无线电信号(RSS)的分布随时间是固定的。然而，随着时间的推移，信号会发生很大的变化，而预先获得的无线电地图(或先验知识)在定位阶段已经过时，从而导致定位精度下降。为了解决这一问题，本文提出了一种与环境无关的DFL方法EIL，该方法在训练阶段和定位阶段都消除了环境对RSS随时间的干扰，从而提高了系统的鲁棒性和定位精度。通过大量的实验和模拟，EIL在90%的位置上，随着时间的推移，定位误差保持在0.5m到0.6m之间。

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Poster abstract: EIL — An environment-independent Device-free Passive Localization approach

Most previous Device-free Passive Localization (DFL) methods are learning based and they assume the distribution of Received Radio Signal (RSS) distorted by an object is fixed across time. However, the signals significantly vary over time and the pre-obtained radio map (or prior knowledge) outdated in the localization phase, thus causing the localization accuracy decrease. To cope with this problem, this poster proposes, EIL, an environment-independent DFL approach which can improve the system robustness and localization accuracy by eliminating the interference of environment on RSS over time in both the training phase and the localization phase. Through both the extensive experiments and simulations, EIL keeps a range of 0.5m to 0.6m localization errors for 90% locations over time.

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IPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks

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