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.

翻译：基于接收信号强度指示（RSSI）的位置指纹定位因其无需安装新基础设施和修改现有设备的优势（尤其是在当前现代建筑中Wi-Fi设备普及且Wi-Fi接入无处不在的背景下），已成为主流的室内定位技术。采用深度神经网络等人工智能/机器学习技术可使位置指纹定位更加精确可靠，尤其适用于大规模多建筑多楼层室内定位场景。然而，深度神经网络在室内定位中的应用依赖于大量经过预处理和精心标注的训练数据。考虑到室内环境下数据采集的困难性，特别是在新冠疫情当前流行的形势下，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：单楼层增强、相邻楼层增强和单建筑增强。与单输出高斯过程（SOGP）不同，MOGP通过联合处理多个接入点（AP）的接收信号强度观测值，能够考虑密集部署AP（如同一楼层AP）之间的相关性。基于当前最先进的RNN室内定位模型和UJIIndoorLoc（最广泛使用的公开多建筑多楼层室内定位数据库）的实验验证了基于MOGP的RSSI数据增强方法的可行性。结果表明，通过使用建筑全部RSSI数据拟合MOGP模型进行增强（即单建筑增强）所训练的RNN模型，其性能优于其他两种增强方法以及直接使用原始UJIIndoorLoc数据库训练的RNN模型，最终实现了8.42米的平均三维定位误差。