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一种用于室内定位的 Wi-Fi 指纹数据库生成的改进算法。

An improved algorithm to generate a Wi-Fi fingerprint database for indoor positioning.

机构信息

College of Information Science and Technology, East China Normal University, Shanghai 200241, China.

出版信息

Sensors (Basel). 2013 Aug 21;13(8):11085-96. doi: 10.3390/s130811085.

DOI:10.3390/s130811085
PMID:23966197
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3812643/
Abstract

The major problem of Wi-Fi fingerprint-based positioning technology is the signal strength fingerprint database creation and maintenance. The significant temporal variation of received signal strength (RSS) is the main factor responsible for the positioning error. A probabilistic approach can be used, but the RSS distribution is required. The Gaussian distribution or an empirically-derived distribution (histogram) is typically used. However, these distributions are either not always correct or require a large amount of data for each reference point. Double peaks of the RSS distribution have been observed in experiments at some reference points. In this paper a new algorithm based on an improved double-peak Gaussian distribution is proposed. Kurtosis testing is used to decide if this new distribution, or the normal Gaussian distribution, should be applied. Test results show that the proposed algorithm can significantly improve the positioning accuracy, as well as reduce the workload of the off-line data training phase.

摘要

基于 Wi-Fi 指纹的定位技术的主要问题是信号强度指纹数据库的创建和维护。接收信号强度 (RSS) 的显著时间变化是导致定位误差的主要因素。可以使用概率方法，但需要 RSS 分布。通常使用高斯分布或经验导出的分布（直方图）。然而，这些分布要么并不总是正确的，要么需要为每个参考点提供大量数据。在某些参考点的实验中已经观察到 RSS 分布的双峰。本文提出了一种基于改进的双峰高斯分布的新算法。峰度检验用于确定应该应用这种新分布还是正态高斯分布。测试结果表明，所提出的算法可以显著提高定位精度，同时减少离线数据训练阶段的工作量。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bbae/3812643/9a17a2b41b2f/sensors-13-11085f7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bbae/3812643/d8f2bf5ca0b6/sensors-13-11085f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bbae/3812643/2a5a2fe1dbb6/sensors-13-11085f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bbae/3812643/e41e51bb4d98/sensors-13-11085f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bbae/3812643/6d3b93957580/sensors-13-11085f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bbae/3812643/69d9d7dfec0b/sensors-13-11085f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bbae/3812643/cc9bb153b837/sensors-13-11085f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bbae/3812643/9a17a2b41b2f/sensors-13-11085f7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bbae/3812643/d8f2bf5ca0b6/sensors-13-11085f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bbae/3812643/2a5a2fe1dbb6/sensors-13-11085f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bbae/3812643/e41e51bb4d98/sensors-13-11085f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bbae/3812643/6d3b93957580/sensors-13-11085f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bbae/3812643/69d9d7dfec0b/sensors-13-11085f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bbae/3812643/cc9bb153b837/sensors-13-11085f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bbae/3812643/9a17a2b41b2f/sensors-13-11085f7.jpg

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