Satellite Attitude Activity Identification using Change-Point and Wavelet Analysis

The ability to detect satellite maneuvers is an integral part of space domain awareness. While satellite maneuver detection typically refers to orbital maneuvers, the ability to detect a change in attitude or spin state is equally important. In many cases, these attitude maneuvers are the predecessor to orbital maneuvers, and can serve as an indication of future behaviors. One means of assessing a satellites attitude and spin state involves analysis of the object’s light curve, which is the temporal history of reflected light off the satellite and collected by an observer. Model-based approaches have been demonstrated to be excellent tools for eliciting attitude information from light curves, but the results are heavily dependent on the accuracy of the input model. An alternative approach, based on a wavelet decomposition of the light curve signal, has been used to identify attitude activity without the need for a well-defined satellite model. Wavelet decomposition allows the frequency of a signal to be assessed over time, providing critical insight into temporal changes of the signal. When applied to a light curve, these changes can be indicative of attitude activities. This paper focuses on methods to determine and assert that a change has occurred using the light curve signals and wavelet decomposition. Various approaches to identify changes are discussed and compared. The practical application of these methods on noisy sensor data under real collection scenarios is also discussed. Use cases considered include a simulation study as well as real world data collection on GOES-16, a Geostationary satellite, during multiple phases of its lifetime.