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| Abstract: |
| Fingerprint-based Bluetooth positioning is a popular indoor positioning technology. However, the change of indoor environment and Bluetooth anchor locations has significant impact on signal distribution, which will result in the decline of positioning accuracy. The widespread extension of Bluetooth positioning is limited by the need of manual effort to collect the fingerprints with position labels for fingerprint database construction and updating. To address this problem, this paper presents an adaptive fingerprint database updating approach. First, the crowdsourced data including the Bluetooth Received Signal Strength (RSS) sequences and the speed and heading of the pedestrian were recorded. Second, the recorded crowdsourced data were fused by the Kalman Filtering (KF), and then fed into the trajectory validity analysis model with the purpose of assigning the unlabeled RSS data with position labels to generate candidate fingerprints. Third, after enough candidate fingerprints were obtained at each Reference Point (RP), the Density-based Spatial Clustering of Applications with Noise (DBSCAN) approach was conducted on both the original and the candidate fingerprints to filter out the fingerprints which had been identified as the noise, and then the mean of fingerprints in the cluster with the largest data volume was selected as the updated fingerprint of the corresponding RP. Finally, the extensive experimental results show that with the increase of the number of candidate fingerprints and update iterations, the fingerprint-based Bluetooth positioning accuracy can be effectively improved. |
| Key words: indoor positioning fingerprint database updating crowdsourced data Bluetooth DBSCAN |
| DOI：10..11916/j.issn.1005-9113.18137 |
| Clc Number:none |
| Fund: |
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| Descriptions in Chinese: |
| 基于指纹的蓝牙定位是一种十分流行的室内定位技术。但是，蓝牙基站的位置或者是环境的变化会对信号分布产生极大的影响，这最终将导致蓝牙定位精度降低。人工收集带位置标签的指纹信息以建立和更新指纹库严重限制了蓝牙定位技术的广泛推广。为了解决这个问题，本文提出了一种指纹库自适应更新的方法。首先，众包数据包含蓝牙接收信号强度（Received Signal Strength，RSS），行人的速度和航向将被记录下来。其次，这些被记录的众包数据将被卡尔曼滤波进行融合用于轨迹有效性分析，来实现为无地理位置标签的RSS分配标签生成候选指纹的目的。第三，当每个参考点获取到了足够候选指纹，每个参考点的原始指纹和候选指纹将进行基于密度的聚类（Density-based Spatial Clustering of Applications with Noise ，DBSCAN）来滤除噪声，同时最大的一个类的指纹均值将被选为对应参考点更新后的指纹。最后，充分的实验结果表明随着候选指纹和更新次数的增加，基于指纹的蓝牙定位精度可以被逐渐提高。 |
