2023/04/13 by Jarred Jordan, Daniel Posada, Jordan, Jarred +7
Engineering · #Astronomical Observations and Instrumentation #FOS: Computer and information sciences #FOS: Physical sciences #Instrumentation and Methods for Astrophysics (astro-ph.IM) #Robotics (cs.RO) #Space Satellite Systems and Control
paper · pdf · doi:10.48550/arxiv.2304.06227
openalex publication_date 2023/04/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
A method of near real-time detection and tracking of resident space objects (RSOs) using a convolutional neural network (CNN) and linear quadratic estimator (LQE) is proposed. Advances in machine learning architecture allow the use of low-power/cost embedded devices to perform complex classification tasks. In order to reduce the costs of tracking systems, a low-cost embedded device will be used to run a CNN detection model for RSOs in unresolved images captured by a gray-scale camera and small telescope. Detection results computed in near real-time are then passed to an LQE to compute tracking updates for the telescope mount, resulting in a fully autonomous method of optical RSO detection and tracking. Keywords: Space Domain Awareness, Neural Networks, Real-Time, Object Detection, Embedded Systems.