Transition Model–driven Unsupervised Localization Framework Based on Crowd-sensed Trajectory Data

The rapid popularization of mobile devices makes it more convenient and cost-efficient to collect synchronized WiFi received signal strength (RSS) and inertial measurement unit sequences by crowdsensing. The transition model has proven to be a promising unsupervised localization approach that captures the transition relationship between the change of RSS signal space and the change of physical space, alleviating the need of extra knowledge for creating radio map. However, it faces two essential challenges in real-world deployments. First, model coverage affects its locating performance, because a specific transition model only represents its local space. Second, the instability of RSS leads to a conflicting relationship between changes of two spaces because of the complex environment and the heterogeneous type of devices. To address these challenges, we propose Lightgbm-CTMM, a novel unsupervised localization framework. First, a clustering method is adopted to capture the expected relationship to ensure robust coverage. Second, direction filter is employed to guarantee that the change in signal space corresponds to the change in physical space. The feasibility and effectiveness of Lightgbm-CTMM are evaluated by extensive experiments, and the locating performance of Lightgbm-CTMM is better than that of conventional approaches. Moreover, Lightgbm-CTMM reduces the work on quality assessment of trajectories.