School of AI Convergence, Sungshin Women's University, Seoul 02844, Korea.
Sensors (Basel). 2022 Oct 3;22(19):7505. doi: 10.3390/s22197505.
Fingerprinting localization is a promising indoor positioning methods thanks to its advantage of using preinstalled infrastructure. For example, WiFi signal strength can be measured by pre-existing WiFi routers. In the offline phase, the fingerprinting localization method first stores of position and RSSI measurement pairs in a dataset. Second, it predicts a target's location by comparing the stored fingerprint database to the current measurement. The database size is normally huge, and data patterns are complicated; thus, an artificial neural network is used to model the relationship of fingerprints and locations. The existing fingerprinting locations, however, have been developed to predict only single locations. In practice, many users may require positioning services, and as such, the core algorithm should be capable of multiple localizations, which is the main contribution of this paper. In this paper, multiple fingerprinting localization is developed based on an artificial neural network and an analysis of the number of targets that can be estimated without loss of accuracy is conducted by experiments.
指纹定位是一种很有前途的室内定位方法，因为它利用了预先安装的基础设施的优势。例如，可以通过现有的 WiFi 路由器来测量 WiFi 信号强度。在离线阶段，指纹定位方法首先将位置和 RSSI 测量对存储在一个数据集。其次，它通过将存储的指纹数据库与当前测量进行比较来预测目标的位置。数据库的大小通常非常大，数据模式也很复杂；因此，使用人工神经网络来模拟指纹和位置之间的关系。然而，现有的指纹定位已经被开发出来，只能预测单个位置。在实际中，许多用户可能需要定位服务，因此，核心算法应该能够进行多个定位，这是本文的主要贡献。在本文中，基于人工神经网络开发了多个指纹定位，并通过实验分析了在不损失精度的情况下可以估计的目标数量。
