A New Weighted Algorithm Based on the Uneven Spatial Resolution of RSSI for Indoor Localization
A New Weighted Algorithm Based on the Uneven Spatial Resolution of RSSI for Indoor Localization
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基于RSSI空间分辨率不均匀的室内定位新加权算法
DOI：
10.1109/access.2018.2837018
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发表时间：
2018-01-01
期刊：
影响因子：
3.9
通讯作者：
Cheng, Kai
中科院分区：
文献类型：
--
作者：
Xue, Weixing;Hua, Xianghong;Cheng, Kai
加权K近邻算法是室内定位中最常用的算法之一。然而，传统的WKNN算法通过接收信号强度指示（RSSI）差值的倒数来对参考点坐标进行加权，由于RSSI与物理距离之间存在指数关系，这种加权不够精确。此外，基于概率模型或数据融合的方法没有考虑Wi-Fi RSSI的不均匀空间分辨率。因此，为了提高传统定位算法的定位精度，提出了一种基于RSSI物理距离的加权定位算法。实验结果表明，该方法在定位精度上明显优于KNN、Euclidian-W-KNN、Manhattan-W-KNN、EWKNN、LiFS和GPR等方法。
The weighted K-nearest neighbor (WKNN) algorithm is one of the most frequently used algorithms for indoor positioning. However, the traditional WKNN algorithm weights the reference points' coordinates by the inverse of the received signal strength indication (RSSI) difference, which is not accurate enough because of the exponential relationship between RSSI and physical distance. Furthermore, methods based on probabilistic model or data fusion do not consider the uneven spatial resolution of the Wi-Fi RSSI. Therefore, in order to improve the positioning accuracy of traditional location algorithms, this paper proposes a new weighted algorithm based on the physical distance of the RSSI. Experiments were conducted in an office building and the results demonstrate that the proposed method considerably outperforms the KNN, Euclidian-W-KNN, Manhattan-W-KNN, EWKNN, LiFS, and GPR in terms of positioning accuracy, which is defined as the cumulative distribution function of position error.