论文标题
用于多构建和多层室内本地化的基于高斯流程的数据增强
Multi-Output Gaussian Process-Based Data Augmentation for Multi-Building and Multi-Floor Indoor Localization
论文作者
论文摘要
基于RSSI的位置指纹印刷成为一种主流室内定位技术，因为它的优势是不需要安装新的基础架构和现有设备的修改，尤其是考虑到Wi-Fi-abled设备的普遍性以及无处不在的Wi-Fi在现代建筑中访问。 DNNS之类的AI/ML技术的使用使位置指纹更加准确和可靠，尤其是用于大规模的多建筑和多层室内室内定位。但是，DNN在室内定位中的应用取决于大量的预处理和故意标记的数据进行培训。考虑到数据收集在室内环境中的难度，尤其是在Covid-19的当前流行病状态下，我们根据基于多输出高斯工艺（MOGP）（即通过相邻的地板，以及单个建筑物）研究了三种不同的RSSI数据增强方法。与单输出高斯流程（SOGP）不同，MOGP可以考虑从多个接入点（AP）相互紧密部署的RSSI观测值之间的相关性（例如，建筑物的同一地板上的AP）通过共同处理。通过基于最先进的RNN室内本地化模型和Ujiindoorloc和Ujiindoorloc（即，使用RNN Indoor Indoor indoor thatabase the RNN训练有素培训的ujiii thato the ujiii thato the ujiii thato the Ujii thato the Ujii thato the ujii thato the Ujiii thato the ujiii the t t ujii the ujii the ujii the ijiii the ijiii the，rnn室内培训的可行性将通过实验来证明拟合MOGP模型（即通过单个建筑物）的建筑物的RSSI数据优于其他两种增强方法以及使用原始Ujiindoorloc数据库训练的RNN模型，从而导致平均三维定位误差为8.42 m。
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.
