  An unscented Kalman filter with joint state and parameter estimation is
proposed for aerodynamics, states and wind conditions for airborne wind energy
converters. The proposed estimator relies on different measurement setups. Due
to the strict economic constraints of wind energy converters, the sensor setups
are chosen with minimal cost and reliability issues in mind. Simulation data
with a high fidelity system model and experimental tests using flight data,
together with wind measurements obtained using a lidar system for altitude wind
measurements, are used for validation. The data was obtained during test
flights of the EnerK\'ite EK30, an airborne wind energy converter currently in
research operation in Brandenburg, Germany. Feasible accuracies were achieved
even with the simplest of setups and illustrate the gain achievable by airborne
sensors. Additionally, the results encourage further research into use of the
obtained wind estimates for site assessment.
