  In this paper, we propose first a mmWave channel tracking algorithm based on
multidimensional orthogonal matching pursuit algorithm (MOMP) using reduced
sparsifying dictionaries, which exploits information from channel estimates in
previous frames. Then, we present an algorithm to obtain the vehicle's initial
location for the current frame by solving a system of geometric equations that
leverage the estimated path parameters. Next, we design an attention network
that analyzes the series of channel estimates, the vehicle's trajectory, and
the initial estimate of the position associated with the current frame, to
generate a refined, high accuracy position estimate. The proposed system is
evaluated through numerical experiments using realistic mmWave channel series
generated by ray-tracing. The experimental results show that our system
provides a 2D position tracking error below 20 cm, significantly outperforming
previous work based on Bayesian filtering.
