mmWave Wi-Fi Trajectory Estimation with Continuous-Time Neural Dynamic Learning

Abstract

We leverage standards-compliant beam training measurements from commercial-of-the-shelf (COTS) 802.11ad/ay devices for localization of a moving object. Two technical challenges need to be addressed: (1) the beam training measurements are intermittent due to beam scanning overhead control and contention-based channel-time allocation, and (2) how to exploit underlying object dynamics to assist the localization. To this end, we formulate the trajectory estimation as a sequence regression problem. We propose a dual-decoder neural dynamic learning framework to simultaneously reconstruct Wi-Fi beam training measurements at irregular time instances and learn the unknown dynamics over the latent space in a continuous-time fashion by enforcing strong supervision at both the coordinate and measurement levels. The proposed method was evaluated on an in-house mmWave Wi-Fi dataset and compared with a range of baseline methods, including traditional machine learning methods and recurrent neural networks.