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Proceedings Paper

A generalized labeled multi-Bernoulli filter for correlated multitarget systems
Author(s): Ronald Mahler
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Paper Abstract

The labeled random finite set (LRFS) theory of B.-T. Vo and B.-N. Vo is the first systematic, theoretically rigorous formulation of true multitarget tracking, and is the basis for the generalized labeled multi-Bernoulli (GLMB) filter (the first implementable and provably Bayes-optimal multitarget tracking algorithm). Several of the author’s earlier papers investigated Bayes filters that propagate the correlations between two unlabeled evolving multitarget systems—but with limited success. In this paper we provide a theoretically rigorous and much more general approach, by devising a GLMB filter that propagates the correlations between two evolving labeled multitarget systems.

Paper Details

Date Published: 27 April 2018
PDF: 12 pages
Proc. SPIE 10646, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVII, 106460C (27 April 2018); doi: 10.1117/12.2305463
Show Author Affiliations
Ronald Mahler, Random Sets, LLC (United States)


Published in SPIE Proceedings Vol. 10646:
Signal Processing, Sensor/Information Fusion, and Target Recognition XXVII
Ivan Kadar, Editor(s)

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