Greedy Reduction Algorithms for Mixtures of Exponential Family
2015 (English)In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 22, no 6, 676-680 p.Article in journal (Refereed) Published
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.
Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2015. Vol. 22, no 6, 676-680 p.
Exponential family; extended target; integral square error; Kullback-Leibler divergence; mixture density; mixture reduction; target tracking
Electrical Engineering, Electronic Engineering, Information Engineering
IdentifiersURN: urn:nbn:se:liu:diva-112990DOI: 10.1109/LSP.2014.2367154ISI: 000345236400005OAI: oai:DiVA.org:liu-112990DiVA: diva2:779192
Funding Agencies|Swedish research council (VR) under ETT [621-2010-4301]; SSF, project CUAS2015-01-122015-01-082015-10-05