Unresolved Resident Space Object (RSO) Characterization Using Time-Frequency Analysis

Defense of U.S. space assets depends on the successful detection and monitoring of potential threats, including the ability to recognize abnormal spacecraft activities. Although work has been done in this area using resolved images from ground- and space-based sensors, methodologies to characterize unresolved resident space objects (RSO) have received little attention. We propose to develop techniques using both one- and two-dimensional wavelet analyses applied to time series of sensor data to allow the characterization and cataloging of RSO. The advantage of wavelet analysis is that it provides a measurement of the local frequency content of the signal or image. This property allows us to characterize the normal operations of the RSO and to detect rapid changes in the intensity signatures caused by orbit and attitude maneuvers, microsatellite deployment, and stealth operations. We will generate simulated observational data sets for a variety of high-interest, unresolved RSO scenarios and convolve these with an appropriate sensor effects model. We will demonstrate the effectiveness of our wavelet algorithms, using both the spatial 2-D intensity distributions from the images and the temporal 1-D intensity light curves, for the characterization and cataloging of the unresolved RSO.