Wavelet covariance analysis for light curve slew maneuver detection

Resident space object maneuver detection is critical for space situational awareness and satellite catalog updates. A predecessor to most non-stationkeeping maneuvers is a slew maneuver, where the satellite will rotate in order to achieve the appropriate orientation prior to the orbital maneuver. During a slew maneuver, the light reflection observed from a ground-based observer will change because of the change in reflection angle between the object and the observer. With information about the signal-to-noise ratio of the light curve measured from a ground-based observer, a wavelet analysis can be used to accurately identify when a slew maneuver is occurring in real time. This paper derives the covariance of the wavelet decomposition, and uses this information to automatically detect when a slew maneuver occurs. This covariance can be useful in other wavelet decomposition applications that have a quantifiable signal-to-noise ratios. Both simulated and real data are used to assess the performance of the approach.