Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/86055
Citations
Scopus Web of Science® Altmetric
?
?
Type: Conference paper
Title: The best fitting multi-Bernoulli filter
Author: Williams, J.L.
Citation: Statistical Signal Processing (SSP), 2014 IEEE Workshop on, 2014, pp.220-223
Publisher: IEEE
Publisher Place: Online
Issue Date: 2014
ISBN: 9781479949755
Conference Name: 2014 IEEE Workshop on Statistical Signal Processing (SSP 14) (29 Jun 2014 - 2 Jul 2014 : Gold Coast, Qld.)
Statement of
Responsibility: 
Jason L. Williams
Abstract: Recent derivations have shown that the full Bayes random finite set filter incorporates a linear combination of multi- Bernoulli distributions. The full filter is intractable as the number of terms in the linear combination grows exponentially with the number of targets; this is the problem of data association. A highly desirable approximation would be to find the multi-Bernoulli distribution that is closest to the full distribution in some sense, such as the set Kullback-Leibler divergence. This paper proposes an approximate method for achieving this, which can be interpreted as an application of the well-known expectation-maximisation (EM) algorithm.
Rights: ©2014 Crown
DOI: 10.1109/SSP.2014.6884615
Appears in Collections:Aurora harvest 7
Electrical and Electronic Engineering publications

Files in This Item:
File Description SizeFormat 
hdl_86055.pdfAccepted version530.85 kBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.