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13 June 2023 Quasi real-time autonomous satellite detection and orbit estimation
Jarred Jordan, Daniel Posada, Matthew Gillette, David Zuehlke, Troy Henderson
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Proceedings Volume 12528, Real-Time Image Processing and Deep Learning 2023; 1252802 (2023) https://doi.org/10.1117/12.2662272
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States
Abstract
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.
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Jarred Jordan, Daniel Posada, Matthew Gillette, David Zuehlke, and Troy Henderson "Quasi real-time autonomous satellite detection and orbit estimation", Proc. SPIE 12528, Real-Time Image Processing and Deep Learning 2023, 1252802 (13 June 2023); https://doi.org/10.1117/12.2662272
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KEYWORDS
Image processing
Satellites
Telescopes
Object detection
Detection and tracking algorithms
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