Wearable and IoT devices requiring positioning and localisation services grow in number exponentially every year. This rapid growth also produces millions of data entries that need to be pre-processed prior to being used in any indoor positioning system to ensure the data quality and provide a high Quality of Service (QoS) to the end-user. In this paper, we offer a novel and straightforward data cleansing algorithm for WLAN fingerprinting radio maps. This algorithm is based on the correlation among fingerprints using the Received Signal Strength (RSS) values and the Access Points (APs)'s identifier. We use those to compute the correlation among all samples in the dataset and remove fingerprints with low level of correlation from the dataset. We evaluated the proposed method on 14 independent publicly-available datasets. As a result, an average of 14% of fingerprints were removed from the datasets. The 2D positioning error was reduced by 2.7% and 3D positioning error by 5.3% with a slight increase in the floor hit rate by 1.2% on average. Consequently, the average speed of position prediction was also increased by 14%.
翻译:需要定位和本地化服务的可穿和IoT设备每年以指数指数增长。 这种快速增长还产生数百万个数据条目,在任何室内定位系统使用之前需要预先处理,以确保数据质量,并向最终用户提供高质量的服务。 在本文中,我们为WLAN指纹无线电地图提供了一个新的、直截了当的数据清理算法。这种算法基于指纹之间的相关性,使用的是收到的信号强度值和接入点标识。我们使用这些数据条目来计算数据集中所有样本的相互关系,并从数据集中去除低相关程度的指纹。我们评估了14个独立的公开数据集的拟议方法。结果,平均有14%的指纹从数据集中移除。2D定位错误减少了2.7%和3D定位错误5.3%,而地面撞击率平均略有上升1.2%。因此,定位预测的平均速度也增加了14%。