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Optical tracking systems typically trade off between astrometric precision and field of view. In this work, we showcase a networked approach to optical tracking using very wide field-of-view imagers that have relatively low astrometric precision on the scheduled OSIRIS-REx slingshot manoeuvre around Earth on 22 Sep 2017. As part of a trajectory designed to get OSIRIS-REx to NEO 101955 Bennu, this flyby event was viewed from 13 remote sensors spread across Australia and New Zealand to promote triangulatable observations. Each observatory in this portable network was constructed to be as lightweight and portable as possible, with hardware based off the successful design of the Desert Fireball Network. Over a 4-h collection window, we gathered 15 439 images of the night sky in the predicted direction of the OSIRIS-REx spacecraft. Using a specially developed streak detection and orbit determination data pipeline, we detected 2 090 line-of-sight observations. Our fitted orbit was determined to be within about 10 km of orbital telemetry along the observed 109 262 km length of OSIRIS-REx trajectory, and thus demonstrating the impressive capability of a networked approach to Space Surveillance and Tracking.
The detection of fireballs streaks in astronomical imagery can be carried out by a variety of methods. The Desert Fireball Network uses a network of cameras to track and triangulate incoming fireballs to recover meteorites with orbits and to build a fireball orbital dataset. Fireball detection is done on-board camera, but due to the design constraints imposed by remote deployment, the cameras are limited in processing power and time. We describe the processing software used for fireball detection under these constrained circumstances. Two different approaches were compared: (1) A single-layer neural network with 10 hidden units that were trained using manually selected fireballs and (2) a more traditional computational approach based on cascading steps of increasing complexity, whereby computationally simple filters are used to discard uninteresting portions of the images, allowing for more computationally expensive analysis of the remainder. Both approaches allowed a full night’s worth of data (over a thousand 36-megapixel images) to be processed each day using a low-power single-board computer. We distinguish between large (likely meteorite-dropping) fireballs and smaller fainter ones (typical ‘shooting stars’). Traditional processing and neural network algorithms both performed well on large fireballs within an approximately 30 000-image dataset, with a true positive detection rate of 96% and 100%, respectively, but the neural network was significantly more successful at smaller fireballs, with rates of 67% and 82%, respectively. However, this improved success came at a cost of significantly more false positives for the neural network results, and additionally the neural network does not produce precise fireball coordinates within an image (as it classifies). Simple consideration of the network geometry indicates that overall detection rate for triangulated large fireballs is calculated to be better than 99.7% and 99.9%, by ensuring that there are multiple double-station opportunities to detect any one fireball. As such, both algorithms are considered sufficient for meteor-dropping fireball event detection, with some consideration of the acceptable number of false positives compared to sensitivity.
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