@inproceedings{d4b71dad9661459491a942e223f13242,
title = "Particle flow SMC delta-GLMB filter",
abstract = "In this paper we derive a particle flow particle filter implementation of the δ-Generalized Labeled Multi-Bernoulli (δ-GLMB) filter. The bootstrap particle filter δ-GLMB suffers from weight degeneracy for high-dimensional state systems or low measurement noise. In order to avoid weight degeneracy, we employ particle flow to produce a measurement-driven importance distribution that serves as a proposal in the δ-GLMB particle filter. Flow-induced proposals are developed for both types of targets encountered in the δ-GLMB filter, i.e., persistent and birth targets. Numerical simulations reflect the improved performance of the proposed filter with respect to classical bootstrap implementations.",
keywords = "Bayesian estimation, particle filter, particle flow, random finite set, target tracking",
author = "Saucan, \{Augustin Alexandru\} and Yunpeng Li and Mark Coates",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 ; Conference date: 05-03-2017 Through 09-03-2017",
year = "2017",
month = jun,
day = "16",
doi = "10.1109/ICASSP.2017.7952984",
language = "English",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4381--4385",
booktitle = "2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings",
}