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Multiple hypothesis tracking using clustered measurements

Wolf, Michael T. and Burdick, Joel W. (2009) Multiple hypothesis tracking using clustered measurements. In: 2009 IEEE International Conference on Robotics and Automation. IEEE , Piscataway, NJ, pp. 3955-3961. ISBN 978-1-4244-2788-8. https://resolver.caltech.edu/CaltechAUTHORS:20190617-110445594

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Abstract

This paper introduces an algorithm for tracking targets whose locations are inferred from clusters of observations. This method, which we call MHTC, expands the traditional multiple hypothesis tracking (MHT) hypothesis tree to include model hypotheses - possible ways the data can be clustered in each time step - as well as ways the measurements can be associated with existing targets across time steps. We present this new hypothesis framework and its probability expressions and demonstrate MHTC's operation in a robotic solution to tracking neural signal sources.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
https://doi.org/10.1109/ROBOT.2009.5152841DOIArticle
Additional Information:© 2009 IEEE. This work was completed at the California Institute of Technology with support from the National Institutes of Health and the Rose Hills Foundation.
Funders:
Funding AgencyGrant Number
NIHUNSPECIFIED
Rose Hills FoundationUNSPECIFIED
Record Number:CaltechAUTHORS:20190617-110445594
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20190617-110445594
Official Citation:M. T. Wolf and J. W. Burdick, "Multiple hypothesis tracking using clustered measurements," 2009 IEEE International Conference on Robotics and Automation, Kobe, 2009, pp. 3955-3961. doi: 10.1109/ROBOT.2009.5152841
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:96470
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:17 Jun 2019 18:11
Last Modified:03 Oct 2019 21:22

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