Back to Search Start Over

Autonomous on-board Near Earth Object detection

Authors :
Philippe Burlina
Bruno Jedynak
D. Edell
M. Chen
N. Mehta
Avigyan Sinha
Purnima Rajan
Gregory D. Hager
Source :
AIPR
Publication Year :
2015
Publisher :
IEEE, 2015.

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.

Details

Database :
OpenAIRE
Journal :
2015 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)
Accession number :
edsair.doi...........2336bb2b0ce48ac043968c793e358a9e
Full Text :
https://doi.org/10.1109/aipr.2015.7444551