  Localization is a fundamental enabler technology for many applications, like
vehicular networks, IoT, and even medicine. While Global Navigation Satellite
Systems solutions offer great performance, they are unavailable in scenarios
like indoor or underwater environments, and, for large networks, the
instrumentation cost is prohibitive. We develop a localization algorithm from
ranges and bearings, suitable for generic mobile networks. Our algorithm is
built on a tight convex relaxation of the Maximum Likelihood position
estimator. To serve positioning to mobile agents, a horizon-based version is
developed accounting for velocity measurements at each agent. To solve the
convex problem, a distributed gradient-based method is provided. This
constitutes an advantage over centralized approaches, which usually exhibit
high latency for large networks and present a single point of failure.
Additionally, the algorithm estimates all required parameters and effectively
becomes parameter-free. Our solution to the dynamic network localization
problem is theoretically well-founded and still easy to understand. We obtain a
parameter-free, outlier-robust and trajectory-agnostic algorithm, with nearly
constant positioning error regardless of the trajectories of agents and
anchors, achieving better or comparable performance to state-of-the-art
methods, as our simulations show. Furthermore, the method is distributed,
convex and does not require any particular anchor configuration.
