Paper Info
Title
Autonomous on-board Near Earth Object detection
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.
Year
DOI
Venue
2015
10.1109/AIPR.2015.7444551
2015 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)
Keywords
Field
DocType
Autonomous On-board Near Earth Object Detection,asteroid population discovery,Earth-based telescopes,substantial city-size damage,asteroid detection algorithms,autonomous on-board image analysis algorithms,Earth-Sun L1-L2 halo orbits,image analysis algorithmic pipeline,on-board asteroid detection,flight-qualified hardware
Population,Near-Earth object,Software deployment,Telescope,Computer science,Remote sensing,Asteroid,Potentially hazardous object,Halo,Spacecraft
Conference
ISSN
Citations 
PageRank 
1550-5219
0
0.34
References 
Authors
6
8
Name
Order
Citations
PageRank
Purnima Rajan192.23
Philippe Burlina233942.48
Min Chen3796.12
D. Edell400.34
Bruno Jedynak546867.51
N. Mehta600.34
Ayushi Sinha7246.72
Hager Gregory D81946159.37