This paper explores the potentialities of deploying vehicular Cooperative Positioning (CP) systems in urban scenarios utilizing real-world data collected via experimental campaigns. We examine the case of two prototype vehicles equipped with LiDAR sensors for perceiving their surrounding environment and with Global Navigation Satellite System (GNSS) receivers for positioning. The considered use case focuses on the cooperative detection of static landmarks, to be used for improving the vehicles’ GNSS positioning. The experimental campaign points out a severe degradation in ego vehicle localization performances due to complex multipath propagation experienced in the urban scenario. To cope with such a problem, we integrate into the CP system a compensation method able to mitigate the position bias originating from the adverse propagating conditions. Experimental results show that integrating the developed compensation into the CP solution enables an accurate detection of the landmark positions, leading to an enhancement of the vehicle localization accuracy.

Global air traffic has been steadily growing since the beginning of the new century, increasing the need for accurate and reliable positioning in real-time tracking of multiple aircrafts. This paper presents an Interacting Multiple Model (IMM) tracking solution and an assessment of a real Wide Area Multilateration (WAM) aircraft tracking scenario, where measurements from distributed Ground Stations (GSs) are gathered by a Central Processing Station (CPS) running the tracker. The assessment considers a main European airport, where a network of 44 GSs is used to monitor a congested area of $300 \times 250 \mathrm{~km}$. Tracking measurements refer to time differences of arrival (TDOAs) computed starting from the time of arrival (TOA) measured over downlink signals. Specifically, this work considers messages sent over the aviation transponder interrogation mode S. We present the results on IMM-based WAM tracking on airborne maneuvering targets, showcasing the improvements with respect to the conventional Automatic Dependent Surveillance - Broadcast (ADS-B) solution based on global navigation satellite systems (GNSSs).

In recent years, cooperative positioning technologies have emerged as promising augmentation systems for providing high-accuracy positioning (HAP) in cooperative intelligent transportation systems (C-ITS). Among the approaches, implicit cooperative positioning (ICP) takes advantage of shared target detections between vehicles to create common reference points for localization refinement. Their performance, however, is limited by reliance on predefined parametric models, low scalability and communication overhead. To address these problems, this paper introduces a deep multi-agent reinforcement learning (MARL) framework modelled as a decentralized-partially observable Markov decision process (Dec-POMDP). We propose an ICP-multi-agent proximal policy optimization (MAPPO) algorithm, where distributed agents (i.e., the connected vehicles) learn their dynamics and those of the surrounding targets by performing belief estimation over dynamic cooperation graphs that are continuously adjusted by de/activating communication links with neighbors agents. A C-ITS scenario is simulated in a CARLA environment accounting for realistic vehicle dynamics and inter-vehicle communications. The findings reveal that our ICPMAPPO algorithm, leveraging dynamic decentralized execution and centralized training, outperforms ICP in terms of positioning accuracy and communication efficiency.