Sensor Scheduling for Object Tracking with UAVs: A Comparison of Scheduling Paradigms

In this paper, we consider the task of simultaneously tracking several spatially distributed ground objects (i.e., vehicles, or animals) with a single UAV equipped with an imaging sensor. The problem regarded is to actively schedule the sequence of single target observations in such a way that the kinematic state (position and velocity) of the objects to be tracked can be estimated with minimal error. The main difficulty in this task is that there is not one single optimal scheduling policy that is appropriate for all geospatial relations between the UAV and the target objects, as the quality of the target localization depends on the relative position of the object to the sensor. While many established scheduling and planning algorithms can generally be applied to this problem, their suitability varies depending on the specifics of the tracking scenario. In order to track multiple objects with only one sensor in changing situations, a trade-off has to be found between a) collecting sensor data in the right place and at the right time to achieve the lowest estimated error of a single object, and b) not focusing perception to a single object and thus neglecting observations of the others. Even if additional environment descriptors are neglected, relevant literature has proven scheduling problems akin to the one at hand to be NP-hard. We identify criteria according to which a scheduling policy can be selected and conclude with an evaluation of the procedure in simulation experiments.