Physics-Informed Convolutional Neural Network for Indoor Localization
- Resource Type
- Conference
- Authors
- Ashqar, Farah; Khoury, Rakan; Wood, Caroline; Yeh, Yi-Hsuan; Seretis, Aristeidis; Sarris, Costas D.
- Source
- 2021 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (APS/URSI) Antennas and Propagation and USNC-URSI Radio Science Meeting (APS/URSI), 2021 IEEE International Symposium on. :659-660 Dec, 2021
- Subject
- Fields, Waves and Electromagnetics
Location awareness
Machine learning algorithms
Computational modeling
Meetings
Machine learning
Wireless access points
Ray tracing
- Language
The received signal strength indicator (RSSI) from wireless access points in indoor environments can be employed for user localization. The accuracy of RSSI-based localization can be greatly improved from advanced knowledge of the propagation characteristics of an environment, via extensive measurements or computationally costly simulations. This paper introduces a machine learning approach, leveraging a convolutional neural network, aimed at producing high-resolution power maps of complex indoor environments through low-cost ray-tracing simulations. The produced power maps are integrated with a k-nearest neighbors (kNN) algorithm that performs user localization. The proposed approach is successfully demonstrated in a localization case study across the floor of an office building at the University of Toronto campus.