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Sensors 2017, 17(12), 2707; https://doi.org/10.3390/s17122707

A Survey of Recent Advances in Particle Filters and Remaining Challenges for Multitarget Tracking

1
School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an 710072, China
2
BISITE Research Group, School of Science, University of Salamanca, 37008 Salamanca, Spain
*
Author to whom correspondence should be addressed.
Received: 12 October 2017 / Revised: 19 November 2017 / Accepted: 20 November 2017 / Published: 23 November 2017
(This article belongs to the Section Intelligent Sensors)
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Abstract

We review some advances of the particle filtering (PF) algorithm that have been achieved in the last decade in the context of target tracking, with regard to either a single target or multiple targets in the presence of false or missing data. The first part of our review is on remarkable achievements that have been made for the single-target PF from several aspects including importance proposal, computing efficiency, particle degeneracy/impoverishment and constrained/multi-modal systems. The second part of our review is on analyzing the intractable challenges raised within the general multitarget (multi-sensor) tracking due to random target birth and termination, false alarm, misdetection, measurement-to-track (M2T) uncertainty and track uncertainty. The mainstream multitarget PF approaches consist of two main classes, one based on M2T association approaches and the other not such as the finite set statistics-based PF. In either case, significant challenges remain due to unknown tracking scenarios and integrated tracking management. View Full-Text
Keywords: particle filter; target tracking; nonlinear filter; Monte Carlo sampling; Bayesian inference particle filter; target tracking; nonlinear filter; Monte Carlo sampling; Bayesian inference
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Wang, X.; Li, T.; Sun, S.; Corchado, J.M. A Survey of Recent Advances in Particle Filters and Remaining Challenges for Multitarget Tracking. Sensors 2017, 17, 2707.

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