2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
Login Paper Search My Schedule Paper Index Help

My ICASSP 2021 Schedule

Note: Your custom schedule will not be saved unless you create a new account or login to an existing account.
  1. Create a login based on your email (takes less than one minute)
  2. Perform 'Paper Search'
  3. Select papers that you desire to save in your personalized schedule
  4. Click on 'My Schedule' to see the current list of selected papers
  5. Click on 'Printable Version' to create a separate window suitable for printing (the header and menu will appear, but will not actually print)

Paper Detail

Paper IDASPS-3.3
Paper Title Online Dynamic Window (ODW) Assisted 2-stage LSTM Indoor Localization for Smart Phones
Authors Mohammadamin Atashi, Arash Mohammadi, Concordia University, Canada
SessionASPS-3: IoT
LocationGather.Town
Session Time:Thursday, 10 June, 13:00 - 13:45
Presentation Time:Thursday, 10 June, 13:00 - 13:45
Presentation Poster
Topic Applied Signal Processing Systems: Signal Processing over IoT [OTH-IoT]
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract 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 model 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.