Online Dynamic Window (ODW) Assisted Two-stage LSTM Frameworks for Indoor Localization
Mohammadamin Atashi, Mohammad Salimibeni, and Arash Mohammadi

TL;DR
This paper introduces an innovative ODW-assisted two-stage LSTM framework for indoor localization using IMU data, significantly reducing computational time while maintaining high accuracy for real-time applications.
Contribution
It proposes three novel ODW mechanisms that enhance LSTM-based indoor localization by reducing processing time and improving accuracy over traditional methods.
Findings
Achieved near-real-time indoor localization with high accuracy.
Significantly reduced computational time compared to traditional LSTM approaches.
Demonstrated effectiveness on real PDR dataset.
Abstract
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)…
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Taxonomy
TopicsIndoor and Outdoor Localization Technologies · Speech and Audio Processing · Advanced Adaptive Filtering Techniques
