This paper investigates the satellite constellation scheduling problem for non-cooperative space object tracking. Different from the traditional earth observation tasks, space object tracking tasks require observation of fast moving targets that may have unpredictable trajectories, and estimation of the position of the targets through cooperative observation of multiple satellites. An agent-based scheduling framework is proposed to enable real-time, distributed scheduling of LEO constellation satellites for tracking non-cooperative space targets. In the proposed framework, a non-linear filtering of the targets’ trajectories is incorporated, and the observation satellites are dynamically allocated according to the status of the satellites and the states of the non-cooperative targets. Specifically, event triggers of tracking space targets for activating task handover among satellites are defined and classified into five categories. The event-trigger processing and task response mechanism are embedded in the proposed scheduling framework. Thereafter, autonomously adjusting the observation-satellite sets can be achieved through distributed negotiation within only a few satellites. The effectiveness of the proposed method is validated by simulating scenarios of tracking targets without or with maneuvers.

Wind speed estimation for rotary-wing vertical take-off and landing (VTOL) UAVs is challenging due to the low accuracy of airspeed sensors, which can be severely affected by the rotor’s down-wash effect. Unlike traditional aerodynamic modeling solutions, in this paper, we present a K Nearest Neighborhood learning-based method which does not require the details of the aerodynamic information. The proposed method includes two stages: an off-line training stage and an on-line wind estimation stage. Only flight data is used for the on-line estimation stage, without direct airspeed measurements. We use Parrot AR.Drone as the testing quadrotor, and a commercial fan is used to generate wind disturbance. Experimental results demonstrate the accuracy and robustness of the developed wind estimation algorithms under hovering conditions.