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Proceedings Paper

A clutter-agnostic generalized labeled multi-Bernoulli filter
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). Like most multitarget trackers, the GLMB filter is based on the assumption that clutter statistics are known a priori. Recent research has introduced RFS filters that are "clutter-agnostic," in the sense that they can address unknown, dynamically evolving clutter. These filters were unlabeled, however. In this paper we devise a clutter-agnostic GLMB (CA-GLMB) filter, based on the Bernoulli clutter-generator concept.

Paper Details

Date Published: 27 April 2018
PDF: 12 pages
Proc. SPIE 10646, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVII, 106460D (27 April 2018); doi: 10.1117/12.2305464
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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