  We consider the downlink transmission in a single cell multiple-input
multiple-output system, in which the user equipment correspond to a vehicle
moving along a given trajectory. This system utilizes millimeter wave channels
characterized by multiple non-line-of-sight (NLoS) components. As it has been
pointed out in several related works, in such systems radio access network
(RAN)-based positioning can effectively improve the positioning accuracy
achieved by Global Navigation Satellite Systems. However, the RAN-based
positioning accuracy is highly dependent on the quality of the channel
estimates, especially if multipath propagation is exploited. Recognizing that
the communication channels between the serving base station and the vehicle as
well as the geographical position of the vehicle can be advantageously modeled
as inter-related autoregressive processes, we propose a two-stage Kalman filter
algorithm employing two intertwined filters for channel tracking, position
tracking and abrupt channel change detection. The first Kalman filter tracks
angles-of-departure and angles-of-arrival associated with the communication
channels, which are used to make a coarse position estimation. The second
Kalman filter tracks the position of the vehicle utilizing the kinematic
parameters of the vehicle. Simulation results clearly show the advantages of
using the proposed scheme, which exploits the memoryful property of both the
communication channels and the geographical positions, as compared to employing
previously proposed single-stage or not properly combined filters in NLoS
environments.
