An estimation approach is presented for kite power systems with groundbased actuation and generation. Line-based estimation of the kite state, including position and heading, limits the achievable cycle efficiency of such airborne wind energy systems due to significant estimation delay and line sag. We propose a filtering scheme to fuse onboard inertial measurements with ground-based line data for ground-based systems in pumping operation. Estimates are computed using an extended Kalman filtering scheme with a sensor-driven kinematic process model which propagates and corrects for inertial sensor biases. We further propose a visual motion tracking approach to extract estimates of the kite position from ground-based video streams. The approach combines accurate object detection with fast motion tracking to ensure long-term object tracking in real time. We present experimental results of the visual motion tracking and inertial sensor fusion on a ground-based kite power system in pumping operation and compare both methods to an existing estimation scheme based on line measurements.

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We present a novel estimation approach for airborne wind energy systems with ground-based control and energy generation. The estimator fuses measurements from an inertial measurement unit attached to a tethered wing and position measurements from a camera as well as line angle sensors in an unscented Kalman filter. We have developed a novel kinematic description for tethered wings to specifically address tether dynamics. The presented approach simultaneously estimates feedback variables for a flight controller as well as model parameters, such as a time-varying delay. We demonstrate the performance of the estimator for experimental flight data and compare it to a state-of-the-art estimator based on inertial measurements.