Mr. Garrett D. Fitzgerald Profile

Garrett D. Fitzgerald

at US Space Force

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Proceedings Article | 27 May 2022 Presentation + Paper

Geosynchronous satellite detection and tracking with WFOV camera arrays using spatiotemporal neural networks (GEO-SPANN)

Garrett Fitzgerald, Ruixu Liu, Vijayan Asari

Proceedings Volume 12101, 1210104 (2022) https://doi.org/10.1117/12.2618047

KEYWORDS: Sensors, Satellites, Cameras, Image processing, Neural networks, Detection and tracking algorithms, Sensor performance, Imaging systems, Surveillance

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Automated monitoring of low resolution, deep-space objects in wide field of view (WFOV) imaging systems can benefit from the improved performance of deep learning object detectors. The PANDORA sensor array, located in Maui at the Air Force Maui Optical and Supercomputing Site, is an exemplar of a scalable imaging architecture that can detect dim deep-space objects while maintaining a WFOV. The PANDORA system captures 20°×120° images of the night sky oriented along the GEO belt at a rate of two frames per minute. Prior work has established a baseline performance for the detection of Geosynchronous Earth Orbit (GEO) satellite objects using classical, feature-based detectors. This work extends GEO object detection and tracking methodologies by implementing a spatio-temporal deep learning architecture (GEO-SPANN), further improving the state of the art in GEO satellite object detection and tracking. GEO-SPANN consists of a learned spatial detector coupled with a tracking algorithm to detect and re-identify space objects in temporal sequences. We present the detection and tracking results of GEO-SPANN on an annotated PANDORA dataset, reporting an overall maximum F1 point of 0.814, corresponding to 0.766 precision and 0.868 recall. GEO-SPANN advances strategies for autonomous detection and tracking of GEO satellites, enabling the PANDORA sensor system to be leveraged for satellite orbit catalog maintenance and anomaly detection.

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