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Proceedings Paper

Particle flow superpositional GLMB filter
Author(s): Augustin-Alexandru Saucan; Yunpeng Li; Mark Coates
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Paper Abstract

In this paper we propose a Superpositional Marginalized δ-GLMB (SMδ-GLMB) filter for multi-target tracking and we provide bootstrap and particle flow particle filter implementations. Particle filter implementations of the marginalized δ-GLMB filter are computationally demanding. As a first contribution we show that for the specific case of superpositional observation models, a reduced complexity update step can be achieved by employing a superpositional change of variables. The resulting SMδ-GLMB filter can be readily implemented using the unscented Kalman filter or particle filtering methods. As a second contribution, we employ particle flow to produce a measurement-driven importance distribution that serves as a proposal in the SMδ-GLMB particle filter. In high-dimensional state systems or for highly- informative observations the generic particle filter often suffers from weight degeneracy or otherwise requires a prohibitively large number of particles. Particle flow avoids particle weight degeneracy by guiding particles to regions where the posterior is significant. Numerical simulations showcase the reduced complexity and improved performance of the bootstrap SMδ-GLMB filter with respect to the bootstrap Mδ-GLMB filter. The particle flow SMδ-GLMB filter further improves the accuracy of track estimates for highly informative measurements.

Paper Details

Date Published: 2 May 2017
PDF: 12 pages
Proc. SPIE 10200, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVI, 102000F (2 May 2017); doi: 10.1117/12.2263236
Show Author Affiliations
Augustin-Alexandru Saucan, McGill Univ. (Canada)
Yunpeng Li, McGill Univ. (Canada)
Mark Coates, McGill Univ. (Canada)


Published in SPIE Proceedings Vol. 10200:
Signal Processing, Sensor/Information Fusion, and Target Recognition XXVI
Ivan Kadar, Editor(s)

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