Multi-floor Positioning Method based on RSSI in Wireless Sensor Networks
Multi-floor Positioning Method based on RSSI in Wireless Sensor Networks
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DOI：
10.23919/sice56594.2022.9905776
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发表时间：
2022-09
期刊：
影响因子：
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通讯作者：
Huizi Zhang;Hayato Fukunaga;Ryo Ishizuka;Tong Li;S. Tateno
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文献类型：
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作者：
Huizi Zhang;Hayato Fukunaga;Ryo Ishizuka;Tong Li;S. Tateno
近年来，随着无线通信技术的飞速发展，室内定位逐渐被应用到许多场合，如复杂的多层商场的定位。在商场的定位中，经常使用Wi-Fi指纹定位方法。此外，在一些研究中，通常使用k-最近邻算法来估计目标点的位置。但k-近邻算法是在整个建筑物的数据库中进行搜索，导致楼层确定精度低，定位误差大，计算量大。为了解决这个问题，应该将建筑物的数据库合理地划分为若干个聚类，并使用与目标点相似度最高的聚类进行定位。因此，本文提出了一种基于信号距离和位置距离的建筑指纹数据库聚类方法。结果表明，与k-近邻算法相比，该算法提高了楼层确定精度，减小了定位误差。
Recently, with the rapid development of wireless communication technologies, indoor positioning has been gradually applied to many occasions, such as positioning in complex multi-floor shopping malls. In the positioning of shopping malls, the Wi-Fi fingerprint positioning method is often used. Moreover, k-nearest neighbor algorithm is commonly used to estimate the position of the target point in some studies. However, the k-nearest neighbor algorithm searched in the database of the whole building, resulting in low accuracy of floor determination, large positioning errors, and large amount of calculation. To solve this problem, the database of a building should be reasonably divided into several clusters, and the clusters with the highest similarity to the target point should be used for positioning. Therefore, in this paper, a building fingerprint database clustering method based on signal distance and position distance is proposed. The results show that compared with the k-nearest neighbor algorithm, the floor determination accuracy is improved, and the positioning error is reduced.