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The data-driven delta-generalized labeled multi-Bernoulli tracker for automatic birth initialization
Author(s): Keith A. LeGrand; Kyle J. DeMars
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Paper Abstract

The δ-generalized labeled multi-Bernoulli (δ-GLMB) tracker is the first multiple hypothesis tracking (MHT)-like tracker that is provably Bayes-optimal. However, in its basic form, the δ-GLMB provides no mechanism for adaptively initializing targets at their first appearance from unlabeled measurements. By introducing a new multitarget likelihood function that accounts for new target appearance, a data-driven δ-GLMB tracker is derived that automatically initializes new targets in the tracker measurement update. Monte Carlo results of simulated multitarget tracking problems demonstrate improved multitarget tracking accuracy over comparable adaptive birth methods.

Paper Details

Date Published: 27 April 2018
PDF: 20 pages
Proc. SPIE 10646, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVII, 1064606 (27 April 2018); doi: 10.1117/12.2304664
Show Author Affiliations
Keith A. LeGrand, Sandia National Labs. (United States)
Kyle J. DeMars, Missouri Univ. of Science and Technology (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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