Autonomous on-board Near Earth Object detection

Rajan, P. ; Burlina, P. ; Chen, M. ; Edell, D. ; Jedynak, B. ; Mehta, N. ; Sinha, A. ; Hager, G. (2015) Autonomous on-board Near Earth Object detection In: 2015 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), 13-15 October 2015, Washington, DC, USA.

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Official URL: https://doi.org/10.1109/AIPR.2015.7444551

Related URL: http://dx.doi.org/10.1109/AIPR.2015.7444551

Abstract

Most large asteroid population discovery has been accomplished to date by Earth-based telescopes. It is speculated that most of the smaller Near Earth Objects (NEOs) that are less than 100 meters in diameter, whose impact can create substantial city-size damage, have not yet been discovered. Many asteroids cannot be detected with an Earth-based telescope given their size and/or their location with respect to the Sun. We are investigating the feasibility of deploying asteroid detection algorithms on-board a spacecraft, thereby minimizing the expense and need to downlink large collection of images. Having autonomous on-board image analysis algorithms enables the deployment of a spacecraft at approximately 0.7 AU heliocentric or Earth-Sun L1/L2 halo orbits, removing some of the challenges associated with detecting asteroids with Earth-based telescopes. We describe an image analysis algorithmic pipeline developed and targeted for on-board asteroid detection and show that its performance is consistent with deployment on flight-qualified hardware.

Item Type:Conference or Workshop Item (Paper)
Source:Copyright of this article belongs to IEEE.
Keywords:Trajectory; Pipelines; Algorithm design and analysis; Joining processes; Telescopes; Earth.
ID Code:139071
Deposited On:15 Sep 2025 10:39
Last Modified:15 Sep 2025 10:39

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