This thesis presents a Cooperative Positioning method which relies on GNSS-based relative ranging measurements. An optimal implementation of the Particle Filter is proposed to integrate the auxiliary ranging measurements and to deal with non-linearity of the system and non-Gaussian measurement models. In the considered scenario, an agent network is developed by implementing a Bernoullian trajectory, where a vehicular target moves along it according to a uniformly accelerated dynamics. The agents consist on stationary peers located in different points of the trajectory. At first, the general Particle Filter model is enhanced by improving the resampling procedure and the particle generation process. Then, by analyzing a cooperative scenario where an agent provides relative ranging measurements to the target at each time instant, it is shown how the optimal Particle Filter can achieve higher accuracy and precision performance with respect to the suboptimal one, by applying non-Gaussian distribution models for the auxiliary measurements..