Collaborative multi-vehicle localization with respect to static/dynamic target from range and velocity measurements
Ioannis Sarras, Julien Marzat, Sylvain Bertrand, Hélène Piet-Lahanier · 2017
We treat the problem of collaborative multi-vehicle localization using time-varying range and relative velocity measurements. The proposed solution combines local nonlinear observers that estimate the relative positions between agents and their neighbors, and cooperative filters that fuse each agent's local estimates to globally localize them with respect to a target (and therefore to each other). Furthermore, we explicitly introduce an estimator that filters the noisy measured signals and feeds the aforementioned observers. Both scenarios of a static as well as a dynamic target are considered. The overall architecture is proved to provide a uniformly globally exponentially converging localization under the assumptions of persistently exciting motion and of a communication topology that contains a directed spanning tree. The efficiency of the results is illustrated through detailed numerical simulations.