Autonomous on-board Near Earth Object detection

P. Rajan, P. Burlina, M. Chen, D. Edell, B. Jedynak, N. Mehta, A. Sinha, G. Hager

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publication2015 IEEE Applied Imagery Pattern Recognition Workshop, AIPR 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781467395588
DOIs
StatePublished - Mar 30 2016
EventIEEE Applied Imagery Pattern Recognition Workshop, AIPR 2015 - Washington, United States
Duration: Oct 13 2015Oct 15 2015

Publication series

Name2015 IEEE Applied Imagery Pattern Recognition Workshop, AIPR 2015

Other

OtherIEEE Applied Imagery Pattern Recognition Workshop, AIPR 2015
Country/TerritoryUnited States
CityWashington
Period10/13/1510/15/15

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

Fingerprint

Dive into the research topics of 'Autonomous on-board Near Earth Object detection'. Together they form a unique fingerprint.

Cite this