Cooperative Localization with Angular Measurements and Posterior Linearization

Abstract

The application of cooperative localization in vehicular networks is attractive to improve accuracy and coverage. 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.

Range and Bearing Data Fusion for Precise Convex Network Localization

“…Reference [27] addresses the singlesource and network localization problems through a convex relaxation of a nonconvex least-squares cost function, and [25] extends this to mobile setups. The very recent work in [28] explores the problem using belief propagation, but relies on linearized approximations. Recently, [26] takes the ML estimator for the original static scenario and considers Gaussian noise for ranges and von Mises-Fisher noise for bearings.…”