(Georgia Institute of Technology, 2015-09)
Flewelling, Brien R.; Murphy, Timothy S.; Rhodes, Andrew P.; Holzinger, Marcus J.; Christian, John A.
This paper will investigate the application of Scale-Space Theory, specifically Curvature
Scale Space, to 1-Dimensional light curve signals generated by reducing imagery
sequences taken from simulated telescopes tasked in various modes. As an
observed object with a variable light curve is viewed from a sensor achieving a perfect
rate track mode, there is a trade between the time fidelity of the reconstructed
signal and integration time required to make accurate detections. As the tracking
error increases, a sensor in a step-stare con-ops for example may trade spatial
samples for intensity information as a function of time. This is commonly seen in
streak observations of tumbling resident space objects. The method presented here
will demonstrate how consistent light curves with maximum time resolution can
be generated from observation sequences with variable tracking error, and sensor
integration times. Additionally, the sparse representation of these signals using
Curvature Scale-Space feature images will be investigated as a means for rapid correlation
of light-curves against a large database. The proposed rapid correlations
could be used to identify variable operating modes of a known object, or to identify
an object as a member of a database using a method dependent on the order of the
number of salient features as opposed to the number of observations.