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Multiple hypothesis tracking using clustered measurements

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2 Author(s)
Wolf, Michael T. ; Jet Propulsion Lab., California Inst. of Technol., Pasadena, CA, USA ; Burdick, J.W.

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.

Published in:

Robotics and Automation, 2009. ICRA '09. IEEE International Conference on

Date of Conference:

12-17 May 2009