Abstract
We consider a wind field estimation problem with multiple quadcopters. The wind field is assumed to affect the motion of the quadcopters in an additive fashion. Starting with a single quadcopter case, we first design an Extended Kalman filter (EKF) for constant and spatial-varying wind estimation. We next extend the EKF wind estimator for multiple quadcopters with directed connected communication graphs. To fuse the estimates of the wind field, we develop a sequential covariance intersection (SCI) method and a sequential weighted exponential product (SWEP) method for constant and spatially-varying wind fields. The effectiveness of the designed partial state fusion methods is validated and compared in simulations for various communication topologies with constant and Rankine wind models.
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