In this paper, we propose distributed Gaussian process regression (GPR) for resource-constrained distributed sensor networks under localization uncertainty. The proposed distributed algorithm, which combines Jacobi over-relaxation (JOR) and discrete-time average consensus (DAC), can effectively handle localization uncertainty as well as limited communication and computation capabilities of distributed sensor networks. We also extend the proposed method hierarchically using sparse GPR to improve its scalability. The performance of the proposed method is verified in numerical simulations against the centralized maximum a posteriori (MAP) solution and a quick-and-dirty solution. We show that the proposed method outperforms the quick-and-dirty solution and achieve an accuracy comparable to the centralized solution.
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March 2015
Research-Article
Distributed Gaussian Process Regression Under Localization Uncertainty
Sungjoon Choi,
Sungjoon Choi
Department of Electrical and Computer Engineering,
Seoul 151-744, Korea
e-mail: sungjoon.choi@cpslab.snu.ac.kr
ASRI, Seoul National University
,Seoul 151-744, Korea
e-mail: sungjoon.choi@cpslab.snu.ac.kr
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Mahdi Jadaliha,
Mahdi Jadaliha
Department of Mechanical Engineering,
East Lansing, MI 48824-1226
e-mail: jadaliha@egr.msu.edu
Michigan State University
,East Lansing, MI 48824-1226
e-mail: jadaliha@egr.msu.edu
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Jongeun Choi,
Jongeun Choi
Department of Mechanical Engineering,
Department of Electrical and Computer Engineering,
East Lansing, MI 48824-1226
e-mail: jchoi@egr.msu.edu
Department of Electrical and Computer Engineering,
Michigan State University
,East Lansing, MI 48824-1226
e-mail: jchoi@egr.msu.edu
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Songhwai Oh
Songhwai Oh
1
Department of Electrical and Computer Engineering,
Seoul 151-744, Korea
e-mail: songhwai.oh@cpslab.snu.ac.kr
ASRI, Seoul National University
,Seoul 151-744, Korea
e-mail: songhwai.oh@cpslab.snu.ac.kr
1Corresponding author.
Search for other works by this author on:
Sungjoon Choi
Department of Electrical and Computer Engineering,
Seoul 151-744, Korea
e-mail: sungjoon.choi@cpslab.snu.ac.kr
ASRI, Seoul National University
,Seoul 151-744, Korea
e-mail: sungjoon.choi@cpslab.snu.ac.kr
Mahdi Jadaliha
Department of Mechanical Engineering,
East Lansing, MI 48824-1226
e-mail: jadaliha@egr.msu.edu
Michigan State University
,East Lansing, MI 48824-1226
e-mail: jadaliha@egr.msu.edu
Jongeun Choi
Department of Mechanical Engineering,
Department of Electrical and Computer Engineering,
East Lansing, MI 48824-1226
e-mail: jchoi@egr.msu.edu
Department of Electrical and Computer Engineering,
Michigan State University
,East Lansing, MI 48824-1226
e-mail: jchoi@egr.msu.edu
Songhwai Oh
Department of Electrical and Computer Engineering,
Seoul 151-744, Korea
e-mail: songhwai.oh@cpslab.snu.ac.kr
ASRI, Seoul National University
,Seoul 151-744, Korea
e-mail: songhwai.oh@cpslab.snu.ac.kr
1Corresponding author.
Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received December 19, 2013; final manuscript received July 16, 2014; published online October 21, 2014. Assoc. Editor: Dejan Milutinovic.
J. Dyn. Sys., Meas., Control. Mar 2015, 137(3): 031007 (11 pages)
Published Online: October 21, 2014
Article history
Received:
December 19, 2013
Revision Received:
July 16, 2014
Citation
Choi, S., Jadaliha, M., Choi, J., and Oh, S. (October 21, 2014). "Distributed Gaussian Process Regression Under Localization Uncertainty." ASME. J. Dyn. Sys., Meas., Control. March 2015; 137(3): 031007. https://doi.org/10.1115/1.4028148
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