Multivehicle Cooperative Localization using a TOA-Based Simulated Annealing Extended Kalman Filter in Urban Canyons

Abstract

This article proposes a new multivehicle cooperative localization approach that combines time of arrival (TOA) with a heuristic simulated annealing extended Kalman filter (SA-EKF) to enhance positioning accuracy and robustness in urban canyons. The method incorporates a path loss model to account for the complex communication environment between vehicles, using TOA measurements for distributed EKF estimation. The integration of a simulated annealing strategy within the method is instrumental in circumventing local minima, thereby facilitating global optimisation. Furthermore, an adaptive weighted filtering correction mechanism is employed to enhance estimation accuracy and system stability. Experimental results conducted on the SUMO simulation platform and in real-world scenarios demonstrate that the proposed method offers certain advantages over existing approaches in complex, noisy environments.