The following work examines a multi-object, multi-sensor nonlinear tracking problem, applied specifically to Space Situational Awareness (SSA). The SSA problem is concerned with the tracking, detection, and cataloging of space objects from both ground and space-based sensors and is characterized by having a large number of satellites to track versus few available sensors to track them. This discrepancy gives rise to situations where sensors have multiple satellites within their view and must decide which to observe and which to ignore within the limited time frame those observations remain available, a process known as 'sensor tasking' or 'sensor network management'. In order to make these tasking decisions, it is necessary to create some form of utility metric to determine which satellites are the most advantageous to observe out of all the possibilities available to all sensors at a particular instant of time. This paper will study the use of utility metrics from the expected information gain for each object-sensor pair as well as the approximated stability of the estimation errors in order to work towards an optimal tasking strategy. Furthermore, the paper will investigate the coupling of these tasking strategies to two nonlinear estimators which will provide state and uncertainty estimates throughout the tracking simulations. The investigation of this coupling will demonstrate that the use of more accurate estimators leads to better overall estimates, not only due to the advantages within the estimation methods, but also from the improvement in tasking decisions due to selection of these estimators.