Duration: 12 months
Synthetic Tracking (ST) relies on acquiring multiple images with exposure times short enough so that the target object is not visibly elongated (streaked) by its own proper motion. These images are stacked with shifts based on assumed target’s velocity in order to improve target’s signal to noise ratio (SNR). ST is a software equivalent of long exposure imaging with non-sidereal tracking telescope, with a benefit of not having to know the target’s proper motion a priori. In the initially proposed case of survey observations, this technique relies on a blind search for target’s velocity vector with multiple trials and errors in order to determine the correct one. ST can also be beneficial in optical, ground based observations of Earth-orbiting objects (Zscheile et al. 2018). It can substantially increase the SNR in survey observations, which can result in detection of objects smaller than the ~10cm. It can also improve the limiting magnitude in tracking observations by aligning images of targets unexpectedly drifting across the field of view and even eliminate the need for non-sidereal telescope tracking, extending the accessibility of SST observations for less advanced and less expensive sensors. ST for satellites should be upgraded to efficiently work with elongated images. This requires the use of an advanced streak detection algorithm to search for a target after each trial of ST is performed. We plan to develop and verify an improved version of a ST algorithm, tailored towards SST observations. Upgraded ST will work efficiently even if streaks are recorded, which may happen for example for targets with uncertain orbits, such as decaying satellites, fragmentation debris or lost satellites. For the purpose of low frame-rate observations an on-line service providing a scalable computational resources is considered while for high frame-rate observations (or low bandwidth connections) an offline solution might be favourable.
