Stochastic sampling based data association

Matthew Travers*, Todd Murphey, Lucy Pao

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

This paper considers how to determine the origin of a single measurement originating from one of a group of objects moving in close proximity. During the time in which measurements are being received, the dynamics of the various objects are the same except for initial conditions. We present a method that uses techniques from filtering theory to represent a distribution using a finite number of parameters. This method, which we call stochastic sampling based data association (SSBDA), is similar to a particle filter but differs in that we use a modified probabilistic data association filter (PDAF) in the propagation of the distribution associated with the object's location. Using the PDAF it is possible to see the effect that the addition of each measurement has on the covariance of the posterior distribution. We discuss how the covariance of the posterior can be used for making decisions on whether or not a particular measurement originated from a predetermined object of interest.

Original languageEnglish (US)
Title of host publicationProceedings of the 2010 American Control Conference, ACC 2010
PublisherIEEE Computer Society
Pages1386-1391
Number of pages6
ISBN (Print)9781424474264
DOIs
StatePublished - 2010

Publication series

NameProceedings of the 2010 American Control Conference, ACC 2010

ASJC Scopus subject areas

  • Control and Systems Engineering

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