This paper presents the application of a distributed attitude and position estimation algorithm to a swarm of cooperating UAVs with heterogeneous sensors on board. The algorithm, based on a Consensus Extended Kalman Filtering (CEKF) to account for nonlinearities, is implemented assuming kinematic relationships. Numerical simulations are presented on different flight scenarios to evaluate the benefits of dealing with prior and novel information in a separate way on the basis of recent theoretical results on CEKF. Inertial and vision sensors are supposed to be mounted on board of the aircraft. Realistic flight scenarios are analyzed in the light of possible time communication delays among the agents.
Attitude and position estimation for an UAV swarm using consensus Kalman filtering / D'Amato, E.; Notaro, I.; Mattei, M.; Tartaglione, G.. - (2015), pp. 519-524. (Intervento presentato al convegno 2nd IEEE International Workshop on Metrology for Aerospace, MetroAeroSpace 2015 tenutosi a ita nel 2015) [10.1109/MetroAeroSpace.2015.7180711].
Attitude and position estimation for an UAV swarm using consensus Kalman filtering
Mattei M.;
2015
Abstract
This paper presents the application of a distributed attitude and position estimation algorithm to a swarm of cooperating UAVs with heterogeneous sensors on board. The algorithm, based on a Consensus Extended Kalman Filtering (CEKF) to account for nonlinearities, is implemented assuming kinematic relationships. Numerical simulations are presented on different flight scenarios to evaluate the benefits of dealing with prior and novel information in a separate way on the basis of recent theoretical results on CEKF. Inertial and vision sensors are supposed to be mounted on board of the aircraft. Realistic flight scenarios are analyzed in the light of possible time communication delays among the agents.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
