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Internet of Things (IoT)-based indoor localization has gained significantpopularity recently to satisfy the ever-increasing requirements of indoorLocation-based Services (LBS). In this context, Inertial Measurement Unit(IMU)-based localization is of interest as it provides a scalable solutionindependent of any proprietary sensors/modules. Existing IMU-basedmethodologies, however, are mainly developed based on statistical heading andstep length estimation techniques that suffer from cumulative error issues andhave extensive computational time requirements limiting their application forreal-time indoor positioning. To address the aforementioned issues, we proposethe Online Dynamic Window (ODW)-assisted two-stage Long Short Term Memory(LSTM) localization framework. Three ODWs are proposed, where the first modeluses a Natural Language Processing (NLP)-inspired Dynamic Window (DW) approach,which significantly reduces the required computational time. The secondframework is developed based on a Signal Processing Dynamic Windowing (SP-DW)approach to further reduce the required processing time of the two-stageLSTM-based model. The third ODW, referred to as the SP-NLP, combines the firsttwo windowing mechanisms to further improve the overall achieved accuracy.Compared to the traditional LSTM-based positioning approaches, which sufferfrom either high tensor computation requirements or low accuracy, the proposedODW-assisted models can perform indoor localization in a near-real time fashionwith high accuracy. Performances of the proposed ODW-assisted models areevaluated based on a real Pedestrian Dead Reckoning (PDR) dataset. The resultsillustrate potentials of the proposed ODW-assisted techniques in achieving highclassification accuracy with significantly reduced computational time, makingthem applicable for near real-time implementations.

Online Dynamic Window (ODW) Assisted Two-stage LSTM Frameworks for Indoor Localization