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There has been a recent surge of interest on smart phone-based indoor localization due to the urgent need for real-time, accurate, and scalable indoor positioning solutions independent of any proprietary sensors/modules. Existing Inertial Measurement Unit (IMU)-based approaches, typically, use statistical and error prone heading and step length estimation techniques rendering them impractical for robust, real-time and accurate indoor positioning. In this regard, the paper takes one step forward to transfer offline IMU-based models to online positioning frameworks. More specifically, inspired by prominent advances in sequential Signal Processing (SP) and Natural Language Processing (NLP) techniques, two near real-time dynamic windowing mechanisms are proposed based on a two stage Long Short-Term Memory (LSTM) localization architecture. The two underlying LSTM architectures are trained with 2100 Action Units (AU). Compared to the traditional LSTM-based positioning approaches suffering from either high tensor computation requirements or low accuracy preventing them for real-time deployment, the proposed Online Dynamic Windowing (ODW) assisted two stage LSTM models can perform localization in a real-time fashion. Performance evaluations based on a real Pedestrian Dead Reckoning (PDR) dataset shows that the proposed model can achieve exceptional classification accuracy of 97.9% and 95.5% for the two underlying LSTMs.

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