Robust Indoor Location Estimation | Muhammad Shahid Jabbar, PhD

Indoor positioning is hard precisely because GPS doesn’t reach indoors. This project developed and tested machine learning and deep learning-based solutions for robust indoor positioning and navigation, including an LSTM-based pedestrian dead reckoning approach for heading distance and direction estimation, reaching a mean error of 0.78 m without relying on external infrastructure (Hussain et al., 2019) (Jabbar et al., 2020).

The work also explored sensor fusion and fingerprinting to improve robustness against signal degradation, combining WiFi fingerprinting with pedestrian dead reckoning (Jabbar et al., 2018) and evaluating Bluetooth-based indoor localization under real-world conditions (Hussain et al., 2017).

References

2020

ICCE-Asia

Indoor Positioning System: Improved deep learning approach based on LSTM and multi-stage activity classification

Muhammad Shahid Jabbar, Ghulam Hussain, and Jun-Dong Cho

In 2020 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia), 2020

PDF

2019

Electronics

Indoor positioning system: A new approach based on LSTM and two stage activity classification

Ghulam Hussain, Muhammad Shahid Jabbar, Jun-Dong Cho, and 1 more author

Electronics, 2019

PDF

2018

ISAAC

For Enhancing Robustness and Precision of WiFi Fingerprinting Indoor Positioning with Pedestrian Dead Reckoning

Muhammad Shahid Jabbar, Ghulam Hussain, Jun-Dong Cho, and 1 more author

In The 6th International Symposium on Advanced and Applied Convergence (ISAAC), 2018

Best Paper Award Bib

Best Paper Award

@inproceedings{jabbar2018wifi,
  title = {For Enhancing Robustness and Precision of WiFi Fingerprinting Indoor Positioning with Pedestrian Dead Reckoning},
  author = {Jabbar, Muhammad Shahid and Hussain, Ghulam and Cho, Jun-Dong and Bae, Sangmin},
  booktitle = {The 6th International Symposium on Advanced and Applied Convergence (ISAAC)},
  year = {2018},
}

2017

IWCIT

Performance analysis of robustness in Bluetooth based indoor localization

Ghulam Hussain, Muhammad Shahid Jabbar, Hyunwoo Kim, and 3 more authors

In The 3rd International Workshop on Convergence Information Technology (IWCIT), 2017

Best Paper Award Bib

Best Paper Award

@inproceedings{hussain2017bluetooth,
  title = {Performance analysis of robustness in Bluetooth based indoor localization},
  author = {Hussain, Ghulam and Jabbar, Muhammad Shahid and Kim, Hyunwoo and Park, Jinwon and Cho, Jun-Dong and Bae, Sangmin},
  booktitle = {The 3rd International Workshop on Convergence Information Technology (IWCIT)},
  year = {2017},
}