This paper proposes a Distributed Moving Horizon Estimator (DMHE) for the Multi-Vehicle system localization problem using Sensor Networks with sporadic measurements. Due to its capability to efficiently exploit environmental information via constraints, the proposed DHME technique is well-suited to better estimate the system state when measurements are available at time instants a priori unknown. Indeed, the use of output constraints can contribute to locally improve estimation accuracy, especially when dealing with sporadic measurements and biased sensors data. A realistic case study is proposed within the Robot Operating System framework and Gazebo to localize a Multi-Vehicle System using an inexpensive Sensor Network with low-computation capabilities. A comparative campaign simulation is performed to confirm the effectiveness of the proposed DMHE algorithm in terms of accuracy, computation time, and constraints handling with respect to existing results.