INFOrmation and Resource Management (INFORM) for Accurate Tracking of Resident Space Objects
Space situation awareness (SSA), including space surveillance and characterization of all space objects and environments, is critical for national and economic security. SSA is the ability to detect, track and characterize passive and active space objects. In light of the large number of Resident Space Objects (RSOs), and the generally accepted notion that our knowledge about the number and nature of most of the objects is severely limited, an unmet and urgent need exists for accurate tracking and characterization of RSOs. A common example involves assigning probabilities of collision of between two different RSOs. For RSO tracking, the core information needed is the orbit parameters and their associated uncertainties specified at a given epoch. This allows for accurate forward prediction but owing to both the nonlinearity of the orbital dynamics and measurement sparsity, the uncertainty associated with RSOs orbit increases in time. Given the fact that none of the prior accidental collision between tracked objects was observed in real time as they occurred, underscores the need for SSA.
This talk will focus on recent development of mathematical and computational approaches for accurate tracking of RSOs within the geostationary (GEO) regime as well as beyond GEO (XGEO). The crux of the work lies in accounting for uncertainties in orbit and sensor models, characterizing the evolution of the uncertainty of the RSO position, and integrating disparate sources of sensor data with the model output using a Bayesian framework. The probability density function associated with state uncertainty is utilized to compute effective information metrics that reflect the information gain associated with ground-based observation platforms. These data driven metrics can be used to pose an optimization problem that provides an optimal sensor schedule to yield useful observations of high valued targets in space. To accommodate the increasing number of sensors and manage the computational challenges associated with the model data fusion process, it is necessary to develop a computational engine that gracefully scales with the resolution of the desired solution. By accurately characterizing the uncertainty associated with both process and measurement models, this work offers systematic design of low-complexity model-data fusion or filtering algorithms with significant improvement in nominal performance and computational effort. Results from studies corresponding to tracking RSOs, where traditional methods either fail or perform very poorly, are considered to assess the reliability and limitations of the newly established methods.
Finally, some results corresponding to application of this framework to other aerospace applications such as reachability analysis for air mobility and surveillance of an area of interest with autonomous agents such as Unmanned Air Vehicles (UAVs) equipped with various sensors will be discussed.