  We develop joint vehicle tracking and road side unit (RSU) selection
algorithms suitable for vehicle-to-infrastructure (V2I) communications. We
first design an analytical framework for evaluating vehicle tracking systems
based on the extended Kalman filter. A simple, yet effective, metric that
quantifies the vehicle tracking performance is derived in terms of the angular
derivative of a dominant spatial frequency. Second, an RSU selection algorithm
is proposed to select a proper RSU that enhances the vehicle tracking
performance. A joint vehicle tracking algorithm is also developed to maximize
the tracking performance by considering sounding samples at multiple RSUs while
minimizing the amount of sample exchange. The numerical results verify that the
proposed vehicle tracking algorithms give better performance than conventional
signal-to-noise ratio-based tracking systems.
