Fingerprint and Assistant Nodes Based Wi-Fi Localization in Complex Indoor Environment
Fingerprint and Assistant Nodes Based Wi-Fi Localization in Complex Indoor Environment
复制标题
复杂室内环境下基于指纹和辅助节点的Wi-Fi定位
DOI：
10.1109/access.2016.2579879
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发表时间：
2016-01-01
期刊：
影响因子：
3.9
通讯作者：
Liu, Zhi
中科院分区：
文献类型：
--
作者：
Li, Qiyue;Li, Wei;Liu, Zhi
随着Wi-Fi的广泛发展，基于接收信号强度（RSS）指纹的室内定位服务越来越受到研究者的关注。在复杂的室内环境中，多径和非视距（NLOS）条件会导致测量值的较大误差，从而降低室内定位精度。本文提出了一种基于指纹和辅助节点协作的Wi-Fi室内定位方法。首先，根据RSS序列的相似性，在未知节点周围精心选择合适的辅助节点，并将它们之间的距离作为辅助信息，提高定位精度。此外，在复杂的室内环境下，导致非视距误差，自适应卡尔曼滤波器的颜色噪声被用来减轻飞行时间的测距误差。实验表明，在复杂的室内环境中，我们的系统可以优于其同行的鲁棒性能和低定位估计误差。
With the extensive development of Wi-Fi, indoor location services based on received signal strength (RSS) fingerprints have attracted increasing attention from researchers. In complex indoor environments, multipath and non-line-of-sight (NLOS) conditions would lead to large errors in measured values, thereby reducing indoor positioning accuracy. In this paper, we propose a Wi-Fi indoor localization method based on collaboration of fingerprint and assistant nodes. First, appropriate assistant nodes based on the similarity of RSS sequences are elaborately selected around the unknown node and distances between them are used as auxiliary information to improve the positioning accuracy. Furthermore, in the complex indoor circumstances that result in NLOS error, an adaptive Kalman filter with colored noise is used to mitigate the time-of-flight ranging error. Experiments demonstrate that in complex indoor environments, our system can outperform its counterparts with robust performance and low localization estimation error.