Implicit Self-Augmentation and Soft Dominance Prediction for Pedestrian Inertial Localization

Neural inertial localization has shown its advantage in simple smartphone usage scenarios; however, dominance learning and the model’s adaptability in intricate scenarios have been left unexplored sufficiently. In this article, we propose a novel implicit self-augmentation and soft dominance prediction method for pedestrian localization, called $\textbf {S}^{2}$ PL. The core idea revolves around a dual-stage learning strategy: 1) a soft dominance predictor (SDP) is first devised to achieve dominance-aware learning and identify dominant features of different sensors at each timestamp t, and afterward, we extend ResMixer to capture spatial contextual information from inertial measurement unit (IMU) measurements, followed by combining dominant features to derive the corresponding robust hybrid representations; and 2) introducing implicit self-augmentation technology (i.e., Mixup) to fine-tune our proposed model for better adaptability. Extensive experiments on three real-world datasets demonstrate that the proposed $\textbf {S}^{2}$ PL obtains substantial performance gain over nine up-to-date state-of-the-art alternatives and makes more than 15% improvements under several scenarios. The real-world inertial tracking trials conducted at the TAIIC campus serve as further validation of the superior generalization capabilities exhibited by our proposed method.