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Título
A Survey of Recent Advances in Particle Filters and Remaining Challenges for Multitarget Tracking
Autor(es)
Materia
Computer science
Particle filter
Target tracking
Nonlinear filter
Monte Carlo sampling
Bayesian inference
Fecha de publicación
2017
Editor
MDPI Publishing (Basilea, Suiza)
Citación
Wang, X., Li, T., Sun, S., Corchado, J.M. (2017). A Survey of Recent Advances in Particle Filters and Remaining Challenges for Multitarget Tracking. Sensors, 17
Resumen
[EN]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.
URI
ISSN
1424-8220
Versión del editor
Colecciones
- BISITE. Artículos [294]