TY - GEN
T1 - Autonomous on-board Near Earth Object detection
AU - Rajan, P.
AU - Burlina, Philippe
AU - Chen, M.
AU - Edell, D.
AU - Jedynak, B.
AU - Mehta, N.
AU - Sinha, A.
AU - Hager, G.
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2016/3/30
Y1 - 2016/3/30
N2 - 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.
AB - 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.
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U2 - 10.1109/AIPR.2015.7444551
DO - 10.1109/AIPR.2015.7444551
M3 - Conference contribution
AN - SCOPUS:84966571201
T3 - 2015 IEEE Applied Imagery Pattern Recognition Workshop, AIPR 2015
BT - 2015 IEEE Applied Imagery Pattern Recognition Workshop, AIPR 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE Applied Imagery Pattern Recognition Workshop, AIPR 2015
Y2 - 13 October 2015 through 15 October 2015
ER -