Cargando…
An Improved Unscented Particle Filter Approach for Multi-Sensor Fusion Target Tracking
In this paper, a new approach of multi-sensor fusion algorithm based on the improved unscented particle filter (IUPF) and a new multi-sensor distributed fusion model are proposed. Additionally, we employ a novel multi-target tracking algorithm that combines the joint probabilistic data association (...
Autores principales: | Luo, Junhai, Wang, Zhiyan, Chen, Yanping, Wu, Man, Yang, Yang |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7730919/ https://www.ncbi.nlm.nih.gov/pubmed/33266020 http://dx.doi.org/10.3390/s20236842 |
Ejemplares similares
-
Unscented Particle Filter Algorithm Based on Divide-and-Conquer Sampling for Target Tracking
por: Du, Sichun, et al.
Publicado: (2021) -
Multi-Sensor Optimal Data Fusion Based on the Adaptive Fading Unscented Kalman Filter
por: Gao, Bingbing, et al.
Publicado: (2018) -
Achieving Adaptive Visual Multi-Object Tracking with Unscented Kalman Filter
por: Zhang, Guowei, et al.
Publicado: (2022) -
Adaptive Unscented Kalman Filter for Target Tacking with Time-Varying Noise Covariance Based on Multi-Sensor Information Fusion
por: Wang, Dapeng, et al.
Publicado: (2021) -
Auxiliary Truncated Unscented Kalman Filtering for Bearings-Only Maneuvering Target Tracking
por: Li, Liang-Qun, et al.
Publicado: (2017)