Greedy Reduction Algorithms for Mixtures of Exponential Family

Ardeshiri T., Granstrom K., Ozkan E., ORGUNER U.

IEEE SIGNAL PROCESSING LETTERS, vol.22, no.6, pp.676-680, 2015 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 22 Issue: 6
  • Publication Date: 2015
  • Doi Number: 10.1109/lsp.2014.2367154
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.676-680
  • Keywords: Exponential family, extended target, integral square error, Kullback-Leibler divergence, mixture density, mixture reduction, target tracking, RANDOM MATRICES, TRACKING, FILTER
  • Middle East Technical University Affiliated: Yes


In this letter, we propose a general framework for greedy reduction of mixture densities of exponential family. The performances of the generalized algorithms are illustrated both on an artificial example where randomly generated mixture densities are reduced and on a target tracking scenario where the reduction is carried out in the recursion of a Gaussian inverse Wishart probability hypothesis density (PHD) filter.