Seminars

The population of objects orbiting the Earth is growing rapidly, increasing the risk of catastrophic collision events and the demand on existing systems for space situational awareness (SSA). Much of this growth is being driven by the construction of proliferated low Earth orbit (PLEO) satellite constellations for global internet connectivity. This dissertation addresses some of the challenges that must be overcome to develop a space surveillance system that can handle this evolving population. This work focuses on the multi-target tracking (MTT) algorithms that process measurements from sensors to estimate the orbits of objects in space. We derive the field-of-view-partitioned Generalized labeled multi-Bernoulli filter (FP-GLMBF), a multiple hypothesis multi-target filter designed for use with sensor networks with limited fields of view (FOVs) and demonstrate how its useful features can be applied to other MTT algorithms to improve their performance in limited-FOV tracking scenarios. We then apply the FP-GLMBF to a simulated SSA scenario that includes a new PLEO constellation and assess its performance in terms of computation time, memory usage, and tracking accuracy. Finally, we derive a method for initial orbit determination (IOD) that enables a multi-target filter to quickly attribute and acquire custody of a newly detected space object that is produced by another object that the filter is already tracking. Our IOD algorithm is applied to simulated breakup and satellite deployment scenarios to demonstrate its effectiveness.