Multiple hypothesis tracking using clustered measurements

Authors:
Michael T. Wolf;Joel W. Burdick
Affiliations:
Jet Propulsion Laboratory, California Institute of Technology;Mechanical Engineering, California Institute of Technology
Venue:
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Year:
2009

Citing 3
Cited 0

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A Robotic Neural Interface for Autonomous Positioning of Extracellular Recording Electrodes

International Journal of Robotics Research

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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.