Evaluation of Estimators for Hybrid GNSS-Terrestrial Localization in Collaborative Networks

Abstract

Global Navigation Satellite Systems (GNSS) constitute the cornerstone for outdoor positioning, which is essential information for prospective automated vehicles. The combination of GNSS with terrestrial ranging, for instance in the form of 5G or UWB, will make accurate positioning a reality even in urban canyon scenarios where GNSS is likely to fail. Thus, hybrid GNSS-terrestrial localization in collaborative networks has become a hotspot for the research community. This paper discusses the Cramér-Rao Bound (CRB) as lower bound for location estimates and evaluates two snapshot estimators, one deterministic and the other Bayesian, for distributed and centralized localization in cooperative networks. The performance of the estimators is evaluated with respect to the presented CRB in a simulated network of mobile and anchor agents, and the role played by agent-to-agent and agentto-anchor ranging is discussed.