An efficient indoor location system in WLAN based on Database Partition and Euclidean Distance-Weighted Pearson Correlation Coefficient

2016 2nd IEEE International Conference on Computer and Communications (ICCC) Pub Date : 2016-10-01 DOI:10.1109/COMPCOMM.2016.7924999

Gong Chen, Qiang Liu, Yunkai Wei, Qin Yu

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引用次数: 6

Abstract

This paper proposes an indoor location system in WLAN based on fingerprint Database Partition and Euclidean Distance-Weighted Pearson Correlation Coefficient which use a new method of partitioning the fingerprint database is PWNN(Nearest Neighbors based on Pearson correlation coefficient and Distance-weighted). This system includes three stages: offline data collection and pretreatment; Online positioning; Fixed positioning. The first stage partitions the fingerprint database in accordance with the maximum signal strength AP (Access Point) to improve the speed of matching. The second stage uses Pearson correlation coefficient to match the signal fingerprint and select the probability of the collection points, then applies NN algorithm and weighted Euclidean distance to estimate the position. The actual system test proves that the fusion algorithm can effectively improve positioning accuracy and greatly shorten positioning time. Thus, it is an effective and valid indoor positioning method.

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基于欧几里得距离加权Pearson相关系数的无线局域网室内高效定位系统

本文提出了一种基于欧几里得距离加权皮尔逊相关系数和指纹数据库分区的无线局域网室内定位系统，该系统采用了一种新的基于皮尔逊相关系数和距离加权的最近邻指纹数据库分区方法。该系统包括三个阶段:离线数据采集和预处理;在线定位;固定定位。第一阶段根据最大信号强度AP (Access Point)对指纹库进行分区，提高匹配速度。第二阶段利用Pearson相关系数对信号指纹进行匹配，选择采集点的概率，然后利用神经网络算法和加权欧氏距离对采集点位置进行估计。实际系统测试证明，融合算法能有效提高定位精度，大大缩短定位时间。因此，它是一种有效的室内定位方法。

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2016 2nd IEEE International Conference on Computer and Communications (ICCC)

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