文章基本信息

标题：Towards Compressive Sampling and Fine-Grained Indoor Fingerprint Positioning
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作者：Chen, Y. ; Yu, D
期刊名称：International Journal of Electronics Communication and Computer Engineering
印刷版ISSN：2249-071X
电子版ISSN：2278-4209
出版年度：2015
卷号：6
期号：1
页码：158-162
出版社：IJECCE
摘要：Location based services (LBS) enable various mobile application and improve the human life significantly. GPS has largely solved the problem for outdoor positioning. However, the accuracy and usability of positioning algorithm remains a grand challenge for indoor scenarios. WiFi-based fingerprint positioning has attracted lots of research attentions, for the advantage of leveraging existing indoor WLAN infrastructure. But the positioning accuracy and fingerprint sampling are both challenge for WiFi-based fingerprint positioning. This paper proposed a trade-off method between accurate positioning and tedious sampling. It first utilizes the Gaussian process model to complete compressive sampling instead of the whole-cell sampling, and then proposed an improved K-Means method based on the similarity threshold to increase the performance accuracy. Our experiment shows our improve method can get better accuracy with 2m median error which approximates results of the real raw data. Meanwhile, our Gaussian process model also largely reduces the tedious sampling
关键词：Towards Compressive Sampling and Fine-Grained Indoor Fingerprint Positioning