  Internet of Things (IoT)-based indoor localization has gained significant
popularity recently to satisfy the ever-increasing requirements of indoor
Location-based Services (LBS). In this context, Inertial Measurement Unit
(IMU)-based localization is of interest as it provides a scalable solution
independent of any proprietary sensors/modules. Existing IMU-based
methodologies, however, are mainly developed based on statistical heading and
step length estimation techniques that suffer from cumulative error issues and
have extensive computational time requirements limiting their application for
real-time indoor positioning. To address the aforementioned issues, we propose
the Online Dynamic Window (ODW)-assisted two-stage Long Short Term Memory
(LSTM) localization framework. Three ODWs are proposed, where the first model
uses a Natural Language Processing (NLP)-inspired Dynamic Window (DW) approach,
which significantly reduces the required computational time. The second
framework is developed based on a Signal Processing Dynamic Windowing (SP-DW)
approach to further reduce the required processing time of the two-stage
LSTM-based model. The third ODW, referred to as the SP-NLP, combines the first
two windowing mechanisms to further improve the overall achieved accuracy.
Compared to the traditional LSTM-based positioning approaches, which suffer
from either high tensor computation requirements or low accuracy, the proposed
ODW-assisted models can perform indoor localization in a near-real time fashion
with high accuracy. Performances of the proposed ODW-assisted models are
evaluated based on a real Pedestrian Dead Reckoning (PDR) dataset. The results
illustrate potentials of the proposed ODW-assisted techniques in achieving high
classification accuracy with significantly reduced computational time, making
them applicable for near real-time implementations.
