Abstract

In this paper, we extend the multiple model track-before-detect method to track all possible target combinations at low signal-to-noise ratios. Given a maximum number of targets, the method estimates the posterior probability density function of the multitarget state vector, the corresponding target existence probabilities, and the probabilities of all possible target combinations. As the particle filter implementation of this method requires a large number of particles to achieve high tracking performance, we propose an efficient partition based proposal function method by partitioning the multiple target space into a set of single target spaces. We also integrate the Markov chain Monte Carlo Metropolis-Hastings method into the particle proposal process to improve sample diversity. The proposed algorithm is validated by tracking five targets in very low signal-to-noise ratios (SNRs).

Original languageEnglish (US)
Title of host publication2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages8008-8012
Number of pages5
ISBN (Print)9781479928927
DOIs
StatePublished - Jan 1 2014
Event2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014 - Florence, Italy
Duration: May 4 2014May 9 2014

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
CountryItaly
CityFlorence
Period5/4/145/9/14

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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