The Use of Short-arc Angle and Angle Rate Data for Deep-space Initial Orbit Determination and Track Association

The population of deep space resident space objects (RSOs) are tracked with sparse resources and thus tracking data are only collected on these objects for a relatively small fraction of their orbit revolution (i.e. a short-arc). This contributes to commonly mistagged or uncorrelated RSOs, and their associated trajectory uncertainties (covariances) to be less physically meaningful. The case of simply updating a catalogued RSO is not treated here, but rather, the problem of reducing a set of collected short arc data on an arbitrary deep space object without a priori information, and from the observations alone, determining its orbit to an acceptable level of accuracy. Fundamentally, this is a problem of data association and track correlation. Milani et al. (2004) and Tommei et al. (2007) have used the concept of admissible regions and attributable vectors for asteroid track association and now intended it for Geocentric RSOs. This paper takes this concept along with a multiple hypothesis filtering approach to determine how well these RSO orbits can be recovered for shortarc data in near realtime and autonomously. Initially, the methods presented here are explored in simulations. While the methods presented here are explored with synthetic data, the basis for the simulations resides in actual data that has yet to be reduced, but whose characteristics are replicated as well as possible to yield results that can be expected using actual data.