Resource-constrained FPGA Design for Satellite Component Feature Extraction

• URL: http://arxiv.org/abs/2301.09055v1
• Date: Sun, 22 Jan 2023 04:49:04 GMT
• Title: Resource-constrained FPGA Design for Satellite Component Feature Extraction
• Authors: Andrew Ekblad and Trupti Mahendrakar and Ryan T. White and Markus Wilde and Isaac Silver and Brooke Wheeler
• Abstract summary: This work proposes use of neural network-based object detection algorithm that can be deployed on a resource-constrained FPGA. Hardware-in-the-loop experiments were performed on the ORION Maneuver Kinematics Simulator at Florida Tech. Results show the FPGA implementation increases the throughput and decreases latency while maintaining comparable accuracy.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The effective use of computer vision and machine learning for on-orbit applications has been hampered by limited computing capabilities, and therefore limited performance. While embedded systems utilizing ARM processors have been shown to meet acceptable but low performance standards, the recent availability of larger space-grade field programmable gate arrays (FPGAs) show potential to exceed the performance of microcomputer systems. This work proposes use of neural network-based object detection algorithm that can be deployed on a comparably resource-constrained FPGA to automatically detect components of non-cooperative, satellites on orbit. Hardware-in-the-loop experiments were performed on the ORION Maneuver Kinematics Simulator at Florida Tech to compare the performance of the new model deployed on a small, resource-constrained FPGA to an equivalent algorithm on a microcomputer system. Results show the FPGA implementation increases the throughput and decreases latency while maintaining comparable accuracy. These findings suggest future missions should consider deploying computer vision algorithms on space-grade FPGAs.

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