Autonomous Satellite Detection and Tracking using Optical Flow
This addresses the problem of autonomous satellite monitoring for space situational awareness, but it is incremental as it applies existing optical flow methods to this domain.
The paper tackles satellite detection and tracking in space images by using optical flow to estimate image velocities, identifying objects with distinct motion from stars as potential satellites. It tests the algorithm on simulated and ground-based imagery and compares commercial and open-source software implementations.
In this paper, an autonomous method of satellite detection and tracking in images is implemented using optical flow. Optical flow is used to estimate the image velocities of detected objects in a series of space images. Given that most objects in an image will be stars, the overall image velocity from star motion is used to estimate the image's frame-to-frame motion. Objects seen to be moving with velocity profiles distinct from the overall image velocity are then classified as potential resident space objects. The detection algorithm is exercised using both simulated star images and ground-based imagery of satellites. Finally, this algorithm will be tested and compared using a commercial and an open-source software approach to provide the reader with two different options based on their need.