Cooperative localization with angular measurements and posterior linearization

The application of cooperative localization in vehicular networks is attractive to improve accuracy and coverage of the positioning. Conventional distance measurements between vehicles are limited by the need for synchronization and provide no heading information of the vehicle. To address this, we present a cooperative localization algorithm using posterior linearization belief propagation (PLBP) utilizing angle-of-arrival (AoA)-only measurements. Simulation results show that both directional and positional root mean squared error (RMSE) of vehicles can be decreased significantly and converge to a low value in a few iterations. Furthermore, the influence of parameters for the vehicular network, such as vehicle density, communication radius, prior uncertainty, and AoA measurements noise, is analyzed

Position Information from Reflecting Surfaces

In the context of positioning an agent with a single-anchor, this contribution focuses on the Fisher information about the position, orientation and clock offset of the agent provided by single-bounce reflections. The availability of prior knowledge of the agent’s environment is taken into account via a prior distribution of the position of virtual anchors, and the rank, intensity and direction of provided information is studied. We show that when no prior knowledge is available, single-bounce reflections offer position information in the direction parallel to the reflecting surface, irrespective of the agent’s and anchor’s locations. We provide a geometrically intuitive explanation of the results and present numerical examples demonstrating their potential implications