Unsupervised Localization Toward Crowdsourced Trajectory Data: A Deep Reinforcement Learning Approach

Crowdsourcing is an effective method to alleviate the burden of conducting a site-survey procedure for localization tasks. However, crowdsourced data is typically inaccurately and scarcely annotated, rendering accurate localization a rather challenging problem. To alleviate this problem, we propose VRLoc, a deep reinforcement learning (DRL)-based unsupervised wireless localization framework using crowdsourced trajectory data. The proposed VRLoc primarily encompasses three components, i.e., a robust K-means (RKM) clustering method for generating a series of virtual reference points (VRPs), DRL for determining the physical layout for VRPs, and online localization based on VRPs. Specifically, the proposed RKM method employs a density-based approach for the initialization of cluster centers, rather than the commonly used random solution, yielding repeatable and reliable VRP generation results. To accurately determine the physical locations for VRPs, we develop a modified soft actor-critic (SAC)-based VRP layout method with multiple objectives, i.e., the connection topology among VRPs, the floor-plan information, and the near-field condition. Then, we effectively predict locations of target users by utilizing classification models to match the online collected samples with the VRPs annotated by physical locations. The proposed framework is advantageous in achieving high-accuracy unsupervised localization, with the VRPs bridging the unlabeled crowdsourced data and physical location space. Both experimental and simulation results demonstrate the effectiveness and superiority of the proposed VRLoc framework as an accurate and practical solution for unsupervised localization.