An Enhanced Transition Model for Unsupervised Localization

Abstract

Unsupervised localization based on received signal strength and inertial measurement unit (RSS + IMU) sequences is an important branch of indoor localization community, among which Transitional Model to predict Motion from signal change (TMM) is a promising method that does not require much prior knowledge, e.g., floor maps. However, there are still many challenging problems existing in TMM. Among them, the computation burden of the model is awfully heavy, and its localization error is also a painful point. In order to solve the above challenges, we propose a novel transition model, called enhanced TMM (ETMM). First, trajectory data enhancement is proposed to enrich the diversity of trajectory data, which improves the robustness of the transition model by allowing the model to learn more comprehensive and detailed information from the environment. Second, the computation burden has been significantly reduced by using effective RSS preprocessing, which reduces the data dimension and the solution domain. Finally, in order to increase the robustness and localization accuracy of the model, we propose a direction matching (DM) constraint to enhance the mapping relationship between the consecutive RSS signals and the one-step motion. Experiments show that ETMM has a better performance compared with the state-of-the-art method in terms of localization accuracy, computation cost, and robustness.