Combination of Statistical Access Point Selection Methods Based on RSSI in Indoor Positioning System
Combination of Statistical Access Point Selection Methods Based on RSSI in Indoor Positioning System
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DOI：
10.9746/jcmsi.12.109
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发表时间：
2019-05
期刊：
影响因子：
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通讯作者：
S. Tateno;Tong Li;Yuehua Wu;Ziyuan Wang
中科院分区：
文献类型：
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作者：
S. Tateno;Tong Li;Yuehua Wu;Ziyuan Wang
最近，随着无线基础设施的广泛发展，以及智能手机成为日常生活中的必需品，室内定位设备和应用变得越来越流行。先前的研究已经提出了几种基于不同无线通信技术的方法。其中，利用接收信号强度指示（RSSI）值的方法和三边测量方法主要用于获得定位结果。然而，由于噪声和环境干扰等因素的影响，RSSI值会出现异常，这些方法的精度都不令人满意。因此，需要一种新的方法来减小定位误差。为了提高三边测量结果上的定位精度，本文将接入点选择方法和核密度估计方法相结合来获得估计点。在实际环境中设计了实验，实验结果表明，该方法能够有效提高定位精度。
: Recently, as wireless infrastructure has developed widely, and smartphones have become necessary in daily life, indoor positioning devices and applications have become more and more popular. Previous studies have proposed several methods based on di ff erent wireless communication technologies. Among them, methods with received signal strength indicator (RSSI) values and trilateration methods are mainly used to obtain positioning results. However, due to abnormal RSSI values caused by noise and influence from the environment, the accuracies of these methods are not satisfying. Therefore, a new method which can reduce the positioning error is necessary. In this paper, to improve the positioning accuracy above trilateration results, an access point selection method and a kernel density estimation method are combined to obtain estimated points. Experiments are designed in actual environments, and the results of which show that the proposed method is su ffi cient for improving the positioning accuracy.