We consider the problem of tracking multiple objects with unknown state parameter information, time-dependent cardinality, and object identity. The problem becomes even more challenging when the unordered measurements have a large number of false alarms due to high noise or clutter. We propose a new approach that exploits the dependent Dirichlet process as the prior on the evolving object-state distributions. At each time step, a Dirichlet process mixture model, integrated with a Markov chain Monte Carlo method, is also used to learn the object-state identity from the measurements and infer the time- dependent object cardinality. A radar tracking example, with ten targets that are present in the observation scene at different times, is used to evaluate the new approach. We also compare it to the labeled multi-Bernoulli filter and demonstrate its improved tracking performance.

Original languageEnglish (US)
Title of host publicationConference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9781538692189
StatePublished - Feb 19 2019
Event52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018 - Pacific Grove, United States
Duration: Oct 28 2018Oct 31 2018

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)1058-6393


Conference52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018
Country/TerritoryUnited States
CityPacific Grove

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications


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