Coupling of estimation and sensor tasking applied to satellite tracking

The tracking of resident space objects has been a topic of recent concern due to the numerous objects that must be monitored with respect to relatively few sensors that can observe them. This discrepancy creates situations in which sensors have multiple objects within their view and must decide which to observe and which to ignore at a particular time, a process known as sensor tasking or sensor network management. Previous studies have suggested calculating information- (or covariance-) based metrics to aid in this process, which rely on uncertainty estimates of an object's location obtained using a nonlinear estimator. In these studies, the coupling between estimation and sensor tasking is investigated using two covariance-based tasking strategies in conjunction with two nonlinear estimators applied within a simple planar satellite-tracking simulation. Results demonstrate that the use of more accurate estimators leads to better overall estimates, not only due to the advantages within the estimation methods, but from the improvement in tasking decisions due to selection of these estimators. In addition, the paper introduces a new utility metric based on approximating stability of the estimation error through estimating a largest Lyapunov exponent, which is shown to outperform an existing Fisher information tasking approach.