基于多输出高斯过程的室内多建筑多楼层定位数据增强
Multi-Output Gaussian Process-Based Data Augmentation for Multi-Building and Multi-Floor Indoor Localization
AI总结：
针对COVID-19下室内定位数据收集困难，提出基于MOGP的三种RSSI数据增强方法，实验证明按单建筑增强UJIIndoorLoc训练RNN模型效果最佳，定位误差8.42米。
摘要：
基于RSSI的位置指纹识别因无需安装新基础设施或修改现有设备，加之现代建筑中支持Wi-Fi设备的普及和无处不在的Wi-Fi接入，已成为主流室内定位技术。使用DNN等AI/ML技术使位置指纹识别更准确可靠，尤其对于大规模多建筑多楼层室内定位。然而，DNN在室内定位中的应用依赖于大量预处理和精心标注的数据进行训练。考虑到室内环境数据收集的困难，特别是在当前COVID-19疫情下，我们研究了三种基于多输出高斯过程（MOGP）的RSSI数据增强方法：按单楼层、按邻近楼层和按单建筑；与单输出高斯过程（SOGP）不同，MOGP能通过集体处理，考虑彼此部署相近的多个接入点（AP）（如同一楼层的AP）的RSSI观测值之间的相关性。基于最先进的RNN室内定位模型和UJIIndoorLoc（最流行的公开多建筑多楼层室内定位数据库）的实验证明了MOGP数据增强的可行性。其中，使用整栋建筑的RSSI数据拟合MOGP模型（即按单建筑）增强UJIIndoorLoc数据库后训练的RNN模型，优于其他两种增强方法及使用原始数据库训练的RNN模型，平均三维定位误差为8.42米。
英文摘要：
Location fingerprinting based on RSSI becomes a mainstream indoor localization technique due to its advantage of not requiring the installation of new infrastructure and the modification of existing devices, especially given the prevalence of Wi-Fi-enabled devices and the ubiquitous Wi-Fi access in modern buildings. The use of AI/ML technologies like DNNs makes location fingerprinting more accurate and reliable, especially for large-scale multi-building and multi-floor indoor localization. The application of DNNs for indoor localization, however, depends on a large amount of preprocessed and deliberately-labeled data for their training. Considering the difficulty of the data collection in an indoor environment, especially under the current epidemic situation of COVID-19, we investigate three different methods of RSSI data augmentation based on Multi-Output Gaussian Process (MOGP), i.e., by a single floor, by neighboring floors, and by a single building; unlike Single-Output Gaussian Process (SOGP), MOGP can take into account the correlation among RSSI observations from multiple Access Points (APs) deployed closely to each other (e.g., APs on the same floor of a building) by collectively handling them. The feasibility of the MOGP-based RSSI data augmentation is demonstrated through experiments based on the state-of-the-art RNN indoor localization model and the UJIIndoorLoc, i.e., the most popular publicly-available multi-building and multi-floor indoor localization database, where the RNN model trained with the UJIIndoorLoc database augmented by using the whole RSSI data of a building in fitting an MOGP model (i.e., by a single building) outperforms the other two augmentation methods as well as the RNN model trained with the original UJIIndoorLoc database, resulting in the mean three-dimensional positioning error of 8.42 m.
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