Heuristic and Optimized Sensor Tasking Observation Strategies with Exemplification for Geosynchronous Objects

With the new space fence technology, the catalog of known space objects is expected to increase to the order of 100,000 objects. Objects need to be initially detected, and sufficient observations need to be collected to allow for a first orbit determination. Furthermore, the objects have to be reobserved regularly, to keep them in the catalog, because the position uncertainty of the objects increases over time, due to unmodeled dynamic effects. Only a small number of ground-based and even fewer space-based sensors are currently available that are able to collect observations, compared to the large number of objects that need to be observed. This makes efficient sensor tasking, which takes into account the realistic ramifications of the problem, crucial in building up and maintaining a precise and accurate catalog of space objects. The time-varying sensor performance and specific sensor constraints are influenced by the sensor location and observational environmental effects, sensor hardware, processing software, and observation modes. This paper shows a new method of solving sensor tasking as an optimization problem translating the heuristic principles that have been successfully applied in sensor tasking of actual space situational awareness networks in a rigorous mathematical framework. A computationally fast near-optimal solution is presented, outperforming traditional heuristic sensor tasking methods. Applications of the methodology are shown via the example of the geosynchronous objects listed in the US Strategic Command two-line element catalog. The results are compared to state-of-the-art observation strategies.