Physics-Informed Convolutional Neural Network for Indoor Localization

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.