# Cooperative Localization with Angular Measurements and Posterior Linearization

## TL;DR

This paper introduces a cooperative localization algorithm for vehicular networks that uses angle-of-arrival measurements and posterior linearization belief propagation to improve accuracy and convergence speed.

## Contribution

It proposes a novel localization method leveraging AoA-only measurements and posterior linearization belief propagation, addressing limitations of traditional distance-based methods.

## Key findings

- Significant reduction in positional and directional RMSE.
- Fast convergence of the localization algorithm within a few iterations.
- Analysis of parameter effects on localization accuracy.

## 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.