Three-Dimensional Cooperative Positioning for VANETs with AOA Measurements

In this paper, positioning performances of vehicular ad-hoc networks (VANETs) are significantly enhanced by cooperative localization (CL) based on the three dimensional (3D) angle-of-arrival (AOA) measurements. Localizers are proposed based on generalized approximate message passing (GAMP), which works under (un-)known Euler rotation angles, and π/2π-periodic azimuth AOAs. Firstly, when the Euler angles are known, GAMP localizer is obtained by categorizing the AOACL problem as a generalized linear mixing one, and then resolved by GAMP, whose mean-and-variance messages involving 3D AOAs are evaluated by importance sampling technique. Secondly, when the Euler angles are unknown, the expectation maximization (EM) framework is combined with GAMP, where vehicle positions and Euler angles are alternatively updated through one-step GAMP iteration and maximizing conditional probability distribution functions (pdfs) expected over hybrid variables, which are obtained by subtracting the xyz position of the vehicle receiving measurements with the position of its pairing neighbor, respectively. Simulation results validate that the proposed localizers outperform existing localizers. Positioning accuracy can be significantly enhanced compared with global navigation satellite system (GNSS).