Article

On-board analysis of uncalibrated data for a spacecraft at mars

Authors:

Rebecca Castano,

Kiri L. Wagstaff,

Timothy M. Stough,

Benyang TangAuthors Info & Claims

KDD '07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 922 - 930

https://doi.org/10.1145/1281192.1281291

Published: 12 August 2007 Publication History

Abstract

Analyzing data on-board a spacecraft as it is collected enables several advanced spacecraft capabilities, such as prioritizing observations to make the best use of limited bandwidth and reacting to dynamic events as they happen. In this paper, we describe how we addressed the unique challenges associated with on-board mining of data as it is collected: uncalibrated data, noisy observations, and severe limitations on computational and memory resources. The goal of this effort, which falls into the emerging application area of spacecraft-based data mining, was to study three specific science phenomena on Mars. Following previous work that used a linear support vector machine (SVM) on-board the Earth Observing 1 (EO-1)spacecraft, we developed three data mining techniques for use on-board the Mars Odyssey spacecraft. These methods range from simple thresholding to state-of-the-art reduced-set SVM technology. We tested these algorithms on archived data in a flight software testbed. We also describe a significant, serendipitous science discovery of this data mining effort: the confirmation of a water ice annulus around the north polar cap of Mars. We conclude with a discussion on lessons learned in developing algorithms for use on-board a spacecraft.

References

[1]

J. L. Bandfield, D. Rogers, M. D. Smith, and P. R. Christensen. Atmospheric correction and surface spectral unit mapping using Thermal Emission Imaging System data. Journal of Geophysical Research, 10(E10):E10008, 2004.

[2]

C. J. C. Burges. Simplified support vector decision rules. In Proc. of the Thirteenth Int'l Conf. on Machine Learning, pages 71--77, 1996.

Digital Library

[3]

R. Castano, D. Mazzoni, N. Tang, T. Doggett, S. Chien, R. Greeley, B.Cichy, and A. Davies. Onboard classifiers for science event detection on a remote sensing spacecraft. In Proc. of the Twelfth Annual SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining, pages 845--851, 2006.

Digital Library

[4]

S. Chien, R. Sherwood, D. Tran, B. Cichy, G. Rabideau, R. Castaño, A. Davies, D. Mandel, S. Frye, B. Trout, S. Shulman, and D. Boyer. Using autonomy flight software to improve science return on Earth Observing One. Journal of Aerospace Computing, Information, and Communication, 2(4):196--216, April 2005.

[5]

P. R. Christensen et al. Morphology and composition of the surface of Mars: Mars Odyssey THEMIS results. Science, 300:2056--2061, June 2003.

[6]

H. Drucker, C. J. Burges, L. Kaufman, A. Smola, and V. Vapnik. Support vector regression machines. In Advances in Neural Information Processing Systems 9, pages 155--161. MIT Press, 1997.

Digital Library

[7]

H. Houben, R. M. Haberle, R. E. Young, and A. P. Zent. Modeling the Martian seasonal water cycle. Journal of Geophysical Research, 10(E4):9069--9083, 1997.

[8]

H. H. Kieffer and T. N. Titus. TES mapping of Mars' north seasonal cap. Icarus, 154:162--180, 2001.

[9]

J. B. MacQueen. Some methods for classification and analysis of multivariate observations. In Proc. of the Fifth Symposium on Math, Statistics, and Probability, volume 1, pages 281--297, Berkeley, CA, 1967. University of California Press.

[10]

D. E. Smith and M. T. Zuber. Seasonal changes in the icecaps of Mars from laser altimetry and gravity. In Proc. of the 13th Int'l Workshop on Laser Ranging: Science Session, 2002.

[11]

M. D. Smith, J. L. Bandfield, P. R. Christensen, and M. I. Richardson. Thermal Emission Imaging System (THEMIS) infrared observations of atmospheric dust and water ice cloud optical depth. Journal of Geophysical Research, 108(E11):5115--5124, 2003.

[12]

B. Tang and D. Mazzoni. Multiclass reduced-set support vector machines. In Proc. of the Twenty-Third Int'l Conf. on Machine Learning, pages 921--928, 2006.

Digital Library

[13]

T. N. Titus. Mars polar cap edges tracked over 3 full Mars years. In Proc. of the 36th Lunar and Planetary Science Conf., Mar. 2005. Abstract 1993.

[14]

K. L. Wagstaff, R. Castaño, S. Chien, A. B. Ivanov, E. Pounders, and T. N. Titus. An onboard data analysis method to track the seasonal polar caps on Mars. In Proc. of the Eighth Int'l Symp. on Artificial Intelligence, Robotics and Automation in Space, 2005.

[15]

K. L. Wagstaff, A. B. Ivanov, T. N. Titus, and R. Castaño. Observations of the north polar water ice annulus on Mars using THEMIS and TES. Planetary and Space Science, 2007. In press.

Cited By

Miralles PThangavel KFulvio Scannapieco AJagadam NBaranwal PFaldu BAbhang RBhatia SBonnart SBhatnagar IBatul BPrasad POrtega-González HJoseph HMore HMorchedi SKumar Panda AZaccaria Di Fraia MWischert DStepanova D(2023)A critical review on the state-of-the-art and future prospects of machine learning for Earth observation operationsAdvances in Space Research10.1016/j.asr.2023.02.02571:12(4959-4986)Online publication date: Jun-2023
https://doi.org/10.1016/j.asr.2023.02.025
Nagle-McNaughton TScuderi LErickson N(2022)Squeezing Data from a Rock: Machine Learning for Martian ScienceGeosciences10.3390/geosciences1206024812:6(248)Online publication date: 15-Jun-2022
https://doi.org/10.3390/geosciences12060248
Wagstaff KDoran GDavies AAnwar SChakraborty SCameron MDaubar IPhillips CTeredesai AKumar VLi YRosales RTerzi EKarypis G(2019)Enabling Onboard Detection of Events of Scientific Interest for the Europa Clipper SpacecraftProceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3292500.3330656(2191-2201)Online publication date: 25-Jul-2019
https://dl.acm.org/doi/10.1145/3292500.3330656
Show More Cited By

Index Terms

On-board analysis of uncalibrated data for a spacecraft at mars
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees

Recommendations

Enabling Onboard Detection of Events of Scientific Interest for the Europa Clipper Spacecraft
KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

Data analysis and machine learning methods have great potential to aid in planetary exploration. Spacecraft often operate at great distances from the Earth, and the ability to autonomously detect features of interest onboard can enable content-sensitive ...
Landing a Spacecraft on Mars

How much software does it take to land a spacecraft safely on Mars, and how do you make all that code reliable? This column describes such a software development process. The first Web extra at http://mars.jpl.nasa.gov/multimedia/videos/movies/...
Mars Ascent Vehicle Flight Analysis

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

KDD '07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2007

1080 pages

ISBN:9781595936097

DOI:10.1145/1281192

General Chair:
Pavel Berkhin
Yahoo!, USA
,
Program Chairs:
Rich Caruana
Cornell University, USA
,
Xindong Wu
University of Vermont, USA

Copyright © 2007 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 August 2007

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Article

Conference

KDD07

Sponsor:

KDD07: The 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 12 - 15, 2007

California, San Jose, USA

Acceptance Rates

KDD '07 Paper Acceptance Rate 111 of 573 submissions, 19%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

9
Total Citations
View Citations
415
Total Downloads

Downloads (Last 12 months)2
Downloads (Last 6 weeks)0

Reflects downloads up to 30 Aug 2024

Other Metrics

View Author Metrics

Citations

Cited By

Miralles PThangavel KFulvio Scannapieco AJagadam NBaranwal PFaldu BAbhang RBhatia SBonnart SBhatnagar IBatul BPrasad POrtega-González HJoseph HMore HMorchedi SKumar Panda AZaccaria Di Fraia MWischert DStepanova D(2023)A critical review on the state-of-the-art and future prospects of machine learning for Earth observation operationsAdvances in Space Research10.1016/j.asr.2023.02.02571:12(4959-4986)Online publication date: Jun-2023
https://doi.org/10.1016/j.asr.2023.02.025
Nagle-McNaughton TScuderi LErickson N(2022)Squeezing Data from a Rock: Machine Learning for Martian ScienceGeosciences10.3390/geosciences1206024812:6(248)Online publication date: 15-Jun-2022
https://doi.org/10.3390/geosciences12060248
Wagstaff KDoran GDavies AAnwar SChakraborty SCameron MDaubar IPhillips CTeredesai AKumar VLi YRosales RTerzi EKarypis G(2019)Enabling Onboard Detection of Events of Scientific Interest for the Europa Clipper SpacecraftProceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3292500.3330656(2191-2201)Online publication date: 25-Jul-2019
https://dl.acm.org/doi/10.1145/3292500.3330656
Chakraborty SBanerjee AGupta SChristensen P(2018)Region of interest aware compressive sensing of THEMIS images and its reconstruction quality2018 IEEE Aerospace Conference10.1109/AERO.2018.8396524(1-11)Online publication date: Mar-2018
https://doi.org/10.1109/AERO.2018.8396524
Chien SSilverman DRabideau GMandl DHengemihle J(2012)A Direct Broadcast Operations Concept for the HyspIRI MissionSpaceOps 2010 Conference10.2514/6.2010-2048Online publication date: 14-Jun-2012
https://doi.org/10.2514/6.2010-2048
Zoumboulakis MRoussos G(2011)Pattern Detection in Extremely Resource-Constrained DevicesReasoning in Event-Based Distributed Systems10.1007/978-3-642-19724-6_9(195-216)Online publication date: 2011
https://doi.org/10.1007/978-3-642-19724-6_9
Wagstaff KKocurek MMazzoni DTang B(2010)Progressive refinement for support vector machinesData Mining and Knowledge Discovery10.1007/s10618-009-0149-y20:1(53-69)Online publication date: 1-Jan-2010
https://dl.acm.org/doi/10.1007/s10618-009-0149-y
Nakasuka STanaka HYairi T(2009)Journal of the Robotics Society of Japan10.7210/jrsj.27.50227:5(502-505)Online publication date: 2009
https://doi.org/10.7210/jrsj.27.502
Granat RWagstaff KBornstein BTang BTurmon M(2009)Simulating and Detecting Radiation-Induced Errors for Onboard Machine LearningProceedings of the Third IEEE International Conference on Space Mission Challenges for Information Technology10.1109/SMC-IT.2009.22(125-131)Online publication date: 19-Jul-2009
https://dl.acm.org/doi/10.1109/SMC-IT.2009.22

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents